



import os
import re
import unicodedata
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


__UpperCAmelCase						  =     logging.get_logger(__name__)
__UpperCAmelCase						  =     {'''vocab_file''': '''spiece.model'''}

__UpperCAmelCase						  =     {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
}

__UpperCAmelCase						  =     {
    '''google/fnet-base''': 5_12,
    '''google/fnet-large''': 5_12,
}

__UpperCAmelCase						  =     '''▁'''


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= VOCAB_FILES_NAMES
						_lowerCAmelCase								= PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase								= ['''input_ids''', '''token_type_ids''']


						def __init__( self						:  Tuple      ,       _a						:  List[str]      ,       _a						:  Any=False      ,       _a						:  List[str]=True      ,       _a						:  str=True      ,       _a						:  Optional[Any]="<unk>"      ,       _a						:  str="[SEP]"      ,       _a						:  Union[str, Any]="<pad>"      ,       _a						:  Dict="[CLS]"      ,       _a						:  int="[MASK]"      ,       _a						:  Optional[Dict[str, Any]] = None      ,       **_a						:  Dict      ,       ):
													# Mask token behave like a normal word, i.e. include the space before it and
													# is included in the raw text, there should be a match in a non-normalized sentence.
													a__:       Dict											=(
													    AddedToken(_a      ,       lstrip=_a      ,       rstrip=_a      ,       normalized=_a			)
													    if isinstance(_a      ,       _a			)
													    else mask_token
													)

													a__:       str											={} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    do_lower_case=_a      ,       remove_space=_a      ,       keep_accents=_a      ,       unk_token=_a      ,       sep_token=_a      ,       pad_token=_a      ,       cls_token=_a      ,       mask_token=_a      ,       sp_model_kwargs=self.sp_model_kwargs      ,       **_a      ,       )

													a__:       List[str]											=do_lower_case
													a__:       List[str]											=remove_space
													a__:       int											=keep_accents
													a__:       Any											=vocab_file

													a__:       Union[str, Any]											=spm.SentencePieceProcessor(**self.sp_model_kwargs			)
													self.sp_model.Load(_a			)


						@property
						def   _lowerCamelCase			( self						:  str			):
													return len(self.sp_model			)


						def   _lowerCamelCase			( self						:  int			):
													a__:       Any											={self.convert_ids_to_tokens(_a			): i for i in range(self.vocab_size			)}
													vocab.update(self.added_tokens_encoder			)
													return vocab


						def __getstate__( self						:  int			):
													a__:       str											=self.__dict__.copy()
													a__:       List[Any]											=None
													return state


						def __setstate__( self						:  Dict      ,       _a						:  List[Any]			):
													a__:       List[Any]											=d

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

													a__:       List[str]											=spm.SentencePieceProcessor(**self.sp_model_kwargs			)
													self.sp_model.Load(self.vocab_file			)


						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  int			):
													if self.remove_space:
																				a__:       List[Any]											=" ".join(inputs.strip().split()			)
													else:
																				a__:       Dict											=inputs
													a__:       Any											=outputs.replace("``"      ,       "\""			).replace("''"      ,       "\""			)

													if not self.keep_accents:
																				a__:       Union[str, Any]											=unicodedata.normalize("NFKD"      ,       _a			)
																				a__:       int											="".join([c for c in outputs if not unicodedata.combining(_a			)]			)
													if self.do_lower_case:
																				a__:       str											=outputs.lower()

													return outputs


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  str			):
													a__:       List[Any]											=self.preprocess_text(_a			)
													a__:       Tuple											=self.sp_model.encode(_a      ,       out_type=_a			)
													a__:       Dict											=[]
													for piece in pieces:
																				if len(_a			) > 1 and piece[-1] == str(","			) and piece[-2].isdigit():
																											a__:       Union[str, Any]											=self.sp_model.EncodeAsPieces(piece[:-1].replace(_a      ,       ""			)			)
																											if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																																		if len(cur_pieces[0]			) == 1:
																																									a__:       Any											=cur_pieces[1:]
																																		else:
																																									a__:       Tuple											=cur_pieces[0][1:]
																											cur_pieces.append(piece[-1]			)
																											new_pieces.extend(_a			)
																				else:
																											new_pieces.append(_a			)

													return new_pieces


						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  Tuple			):
													return self.sp_model.PieceToId(_a			)


						def   _lowerCamelCase			( self						:  Any      ,       _a						:  List[Any]			):
													return self.sp_model.IdToPiece(_a			)


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  Tuple			):
													a__:       Tuple											=[]
													a__:       Tuple											=""
													a__:       Union[str, Any]											=False
													for token in tokens:
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											if not prev_is_special:
																																		out_string += " "
																											out_string += self.sp_model.decode(_a			) + token
																											a__:       Optional[Any]											=True
																											a__:       Any											=[]
																				else:
																											current_sub_tokens.append(_a			)
																											a__:       int											=False
													out_string += self.sp_model.decode(_a			)
													return out_string.strip()


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  List[int]      ,       _a						:  bool = False      ,       _a						:  bool = None      ,       _a						:  bool = True      ,       **_a						:  Optional[int]      ,       ):
													a__:       Optional[Any]											=kwargs.pop("use_source_tokenizer"      ,       _a			)

													a__:       List[str]											=self.convert_ids_to_tokens(_a      ,       skip_special_tokens=_a			)

													# To avoid mixing byte-level and unicode for byte-level BPT
													# we need to build string separately for added tokens and byte-level tokens
													# cf. https://github.com/huggingface/transformers/issues/1133
													a__:       str											=[]
													a__:       int											=[]
													for token in filtered_tokens:
																				if skip_special_tokens and token in self.all_special_ids:
																											continue
																				if token in self.added_tokens_encoder:
																											if current_sub_text:
																																		sub_texts.append(self.convert_tokens_to_string(_a			)			)
																																		a__:       List[str]											=[]
																											sub_texts.append(_a			)
																				else:
																											current_sub_text.append(_a			)
													if current_sub_text:
																				sub_texts.append(self.convert_tokens_to_string(_a			)			)

													# Mimic the behavior of the Rust tokenizer:
													# No space after <unk>
													if spaces_between_special_tokens:
																				a__:       Any											=re.sub(r"(<unk>) "      ,       r"\1"      ,       " ".join(_a			)			)
													else:
																				a__:       int											="".join(_a			)

													a__:       Optional[int]											=(
													    clean_up_tokenization_spaces
													    if clean_up_tokenization_spaces is not None
													    else self.clean_up_tokenization_spaces
													)
													if clean_up_tokenization_spaces:
																				a__:       Tuple											=self.clean_up_tokenization(_a			)
																				return clean_text
													else:
																				return text


						def   _lowerCamelCase			( self						:  Optional[int]      ,       _a						:  List[int]      ,       _a						:  Optional[List[int]] = None			):
													a__:       List[Any]											=[self.sep_token_id]
													a__:       List[str]											=[self.cls_token_id]
													if token_ids_a is None:
																				return cls + token_ids_a + sep
													return cls + token_ids_a + sep + token_ids_a + sep


						def   _lowerCamelCase			( self						:  Optional[Any]      ,       _a						:  List[int]      ,       _a						:  Optional[List[int]] = None      ,       _a						:  bool = False			):

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

													if token_ids_a is not None:
																				return [1] + ([0] * len(_a			)) + [1] + ([0] * len(_a			)) + [1]
													return [1] + ([0] * len(_a			)) + [1]


						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  List[int]      ,       _a						:  Optional[List[int]] = None			):
													a__:       Dict											=[self.sep_token_id]
													a__:       int											=[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			) * [0] + len(token_ids_a + sep			) * [1]







						def   _lowerCamelCase			( self						:  Tuple      ,       _a						:  str      ,       _a						:  Optional[str] = None			):
													if not os.path.isdir(_a			):
																				logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
																				return
													a__:       str											=os.path.join(
													    _a      ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

													if os.path.abspath(self.vocab_file			) != os.path.abspath(_a			) and os.path.isfile(self.vocab_file			):
																				copyfile(self.vocab_file      ,       _a			)
													elif not os.path.isfile(self.vocab_file			):
																				with open(_a      ,       "wb"			) as fi:
																											a__:       List[str]											=self.sp_model.serialized_model_proto()
																											fi.write(_a			)

													return (out_vocab_file,)










import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__UpperCAmelCase						  =     10_00_00_00_00_00
__UpperCAmelCase						  =     1_00_00

__UpperCAmelCase  ,							__UpperCAmelCase						  =     os.path.split(__file__)
__UpperCAmelCase						  =     os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  int						):
							a__:       Any											=pa.Table.from_pydict({"col": [0] * chunk_size}						)
							a__:       Optional[Any]											=pa.concat_tables([table] * (num_examples // chunk_size)						)
							return datasets.Dataset(__magic_name__     , fingerprint="table_100B"						)


@dataclass
class 	lowerCamelCase__ :
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Tuple											=np.random.default_rng(self.seed			)
													a__:       Tuple											=rng.integers(low=self.low      ,       high=self.high      ,       size=self.size			).tolist()


						def __iter__( self						:  Any			):
													return iter(self._sampled_values			)







						def __len__( self						:  Any			):
													return self.size





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Union[str, Any]											=dataset[0]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Optional[Any]											=dataset[-1]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       List[str]											=dataset[range(len(__magic_name__						) // 2     , len(__magic_name__						) // 2 + 1_024						)]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Optional[int]											=dataset[RandIter(0     , len(__magic_name__						)     , 1_024     , seed=42						)]





def 						__lowerCamelCase       (      ):
							a__:       Dict											={"num examples": SPEED_TEST_N_EXAMPLES}
							a__:       Union[str, Any]											=(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
							print("generating dataset"						)
							a__:       Dict											=generate_100B_dataset(num_examples=__magic_name__     , chunk_size=__magic_name__						)
							print("Functions"						)
							for func in functions:
														print(func.__name__						)
														a__:       Dict											=func(__magic_name__						)

							with open(__magic_name__     , "wb"						) as f:
														f.write(json.dumps(__magic_name__						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()





