






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

import sentencepiece as spm

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


__magic_name__:					Optional[Any]							=     logging.get_logger(__name__)

__magic_name__:					List[str]							=     "▁"

__magic_name__:					Union[str, Any]							=     {"vocab_file": "sentencepiece.bpe.model"}

__magic_name__:					Tuple							=     {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__magic_name__:					Tuple							=     {
    "facebook/xglm-564M": 2_048,
}





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					int											=   VOCAB_FILES_NAMES
	lowercase__						:					List[Any]											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Tuple											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						:					List[Any]											=   ['''input_ids''', '''attention_mask''']
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__="<s>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="</s>"      ,       lowerCAmelCase__="<s>"      ,       lowerCAmelCase__="<unk>"      ,       lowerCAmelCase__="<pad>"      ,       lowerCAmelCase__ = None      ,       **lowerCAmelCase__      ,       )							->				None:
							__magic_name__      :       Any       						= {} if sp_model_kwargs is None else sp_model_kwargs

							# Compatibility with the original tokenizer
							__magic_name__      :       List[Any]       						= 7
							__magic_name__      :       Any       						= [F'<madeupword{i}>' for i in range(self.num_madeup_words			)]

							__magic_name__      :       Union[str, Any]       						= kwargs.get("""additional_special_tokens"""      ,       []			)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

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

							__magic_name__      :       str       						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
							self.sp_model.Load(str(lowerCAmelCase__			)			)
							__magic_name__      :       List[str]       						= 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'

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

							# Mimic fairseq token-to-id alignment for the first 4 token
							__magic_name__      :       Optional[int]       						= {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

							__magic_name__      :       str       						= len(self.sp_model			)
							__magic_name__      :       List[Any]       						= {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words			)}
							self.fairseq_tokens_to_ids.update(lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
	def __getstate__(    self			)							->				str:
							__magic_name__      :       str       						= self.__dict__.copy()
							__magic_name__      :       int       						= None
							__magic_name__      :       str       						= self.sp_model.serialized_model_proto()
							return state
	def __setstate__(    self      ,       lowerCAmelCase__			)							->				Optional[int]:
							__magic_name__      :       Any       						= d

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

							__magic_name__      :       Optional[int]       						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:

							if token_ids_a is None:
													return [self.sep_token_id] + token_ids_a
							__magic_name__      :       Tuple       						= [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = 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__			))
							return [1] + ([0] * len(lowerCAmelCase__			)) + [1, 1] + ([0] * len(lowerCAmelCase__			))
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       List[Any]       						= [self.sep_token_id]

							if token_ids_a is None:
													return len(sep + token_ids_a			) * [0]
							return len(sep + token_ids_a + sep + sep + token_ids_a			) * [0]
	@property
	def       __magic_name__    (    self			)							->				int:
							return len(self.sp_model			) + self.fairseq_offset + self.num_madeup_words
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Dict       						= {self.convert_ids_to_tokens(lowerCAmelCase__			): i for i in range(self.vocab_size			)}
							vocab.update(self.added_tokens_encoder			)
							return vocab
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				List[str]:
							return self.sp_model.encode(lowerCAmelCase__      ,       out_type=lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Dict:
							if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
							__magic_name__      :       Optional[Any]       						= self.sp_model.PieceToId(lowerCAmelCase__			)

							# 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       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Dict:
							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       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       Tuple       						= """""".join(lowerCAmelCase__			).replace(lowerCAmelCase__      ,       """ """			).strip()
							return out_string







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
							if not os.path.isdir(lowerCAmelCase__			):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
													return
							__magic_name__      :       List[str]       						= 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__			) 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:
																			__magic_name__      :       int       						= self.sp_model.serialized_model_proto()
																			fi.write(lowerCAmelCase__			)

							return (out_vocab_file,)








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
