


"""simple docstring"""
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


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

SCREAMING_SNAKE_CASE :    Optional[Any]									=				"""▁"""

SCREAMING_SNAKE_CASE :    Optional[int]									=				{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

SCREAMING_SNAKE_CASE :    List[Any]									=				{
    """facebook/xglm-564M""": 2048,
}



class 			_UpperCAmelCase (							__A				):






		'''simple docstring'''

		lowerCamelCase__				=VOCAB_FILES_NAMES
		lowerCamelCase__				=PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__				=["input_ids", "attention_mask"]



		def __init__(self				,       a_				,       a_="<s>"				,       a_="</s>"				,       a_="</s>"				,       a_="<s>"				,       a_="<unk>"				,       a_="<pad>"				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Dict						=				{} if sp_model_kwargs is None else sp_model_kwargs

			# Compatibility with the original tokenizer
			__snake_case	:						Optional[Any]						=				7
			__snake_case	:						Any						=				[f"""<madeupword{i}>""" for i in range(self.num_madeup_words					)]

			__snake_case	:						List[str]						=				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=_UpperCamelCase				,       eos_token=_UpperCamelCase				,       unk_token=_UpperCamelCase				,       sep_token=_UpperCamelCase				,       cls_token=_UpperCamelCase				,       pad_token=_UpperCamelCase				,       sp_model_kwargs=self.sp_model_kwargs				,       **_UpperCamelCase				,       )

			__snake_case	:						List[str]						=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
			self.sp_model.Load(str(_UpperCamelCase					)					)
			__snake_case	:						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
			__snake_case	:						List[str]						=				1

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

			__snake_case	:						Union[str, Any]						=				len(self.sp_model					)
			__snake_case	:						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(_UpperCamelCase					)

			__snake_case	:						List[Any]						=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



		def __getstate__(self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				self.__dict__.copy()
			__snake_case	:						List[str]						=				None
			__snake_case	:						Dict						=				self.sp_model.serialized_model_proto()
			return state



		def __setstate__(self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				d

			# for backward compatibility
			if not hasattr(self				,       '''sp_model_kwargs'''					):
				__snake_case	:						List[str]						=				{}

			__snake_case	:						Tuple						=				spm.SentencePieceProcessor(**self.sp_model_kwargs					)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if token_ids_a is None:
				return [self.sep_token_id] + token_ids_a
			__snake_case	:						Optional[Any]						=				[self.sep_token_id]
			return sep + token_ids_a + sep + sep + token_ids_a



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = False					):





			'''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 [1] + ([0] * len(_UpperCamelCase					))
			return [1] + ([0] * len(_UpperCamelCase					)) + [1, 1] + ([0] * len(_UpperCamelCase					))



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			__snake_case	:						Dict						=				[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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return len(self.sp_model					) + self.fairseq_offset + self.num_madeup_words



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			if token in self.fairseq_tokens_to_ids:
				return self.fairseq_tokens_to_ids[token]
			__snake_case	:						List[str]						=				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 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''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 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				''''''.join(_UpperCamelCase					).replace(_UpperCamelCase				,       ''' '''					).strip()
			return out_string



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if not os.path.isdir(_UpperCamelCase					):
				logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
				return
			__snake_case	:						List[Any]						=				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:
					__snake_case	:						str						=				self.sp_model.serialized_model_proto()
					fi.write(_UpperCamelCase					)

			return (out_vocab_file,)








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)