
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


SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__     :    int      							=					{
    """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""",
    },
}

SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					{
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

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

class      lowerCAmelCase__  (			a__    ):
  a__ :		Union[str, Any]   						=	VOCAB_FILES_NAMES
  a__ :		List[Any]   						=	PRETRAINED_VOCAB_FILES_MAP
  a__ :		List[str]   						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__ :		Tuple   						=	["""input_ids""", """token_type_ids"""]






  def __init__(	self	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							str=False							,    SCREAMING_SNAKE_CASE__	:							Tuple=True							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=True							,    SCREAMING_SNAKE_CASE__	:							Any="<unk>"							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]="[SEP]"							,    SCREAMING_SNAKE_CASE__	:							Dict="<pad>"							,    SCREAMING_SNAKE_CASE__	:							List[str]="[CLS]"							,    SCREAMING_SNAKE_CASE__	:							Tuple="[MASK]"							,    SCREAMING_SNAKE_CASE__	:							str = None							,    **SCREAMING_SNAKE_CASE__	:							str							,    )  ->							None:
      __lowerCamelCase					      =			(
          AddedToken(__A							,    lstrip=__A							,    rstrip=__A							,    normalized=__A						)
          if isinstance(__A							,    __A						)
          else mask_token
      )

      __lowerCamelCase					      =			{} 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							,    )

      __lowerCamelCase					      =			do_lower_case
      __lowerCamelCase					      =			remove_space
      __lowerCamelCase					      =			keep_accents
      __lowerCamelCase					      =			vocab_file

      __lowerCamelCase					      =			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(__A						)






  @property
  def 				__A (	self	:							List[Any]						)  ->							int:
      return len(self.sp_model						)






  def 				__A (	self	:							List[Any]						)  ->							int:
      __lowerCamelCase					      =			{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	:							Tuple						)  ->							Optional[Any]:
      __lowerCamelCase					      =			self.__dict__.copy()
      __lowerCamelCase					      =			None
      return state






  def __setstate__(	self	:							str							,    SCREAMING_SNAKE_CASE__	:							Dict						)  ->							Dict:
      __lowerCamelCase					      =			d

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

      __lowerCamelCase					      =			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(self.vocab_file						)






  def 				__A (	self	:							Any							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							int:







      if self.remove_space:
          __lowerCamelCase					      =			" ".join(inputs.strip().split()						)
      else:
          __lowerCamelCase					      =			inputs
      __lowerCamelCase					      =			outputs.replace('''``'''							,    '''\"'''						).replace('''\'\''''							,    '''\"'''						)

      if not self.keep_accents:
          __lowerCamelCase					      =			unicodedata.normalize('''NFKD'''							,    __A						)
          __lowerCamelCase					      =			"".join([c for c in outputs if not unicodedata.combining(__A						)]						)
      if self.do_lower_case:
          __lowerCamelCase					      =			outputs.lower()

      return outputs






  def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]						)  ->							List[str]:
      __lowerCamelCase					      =			self.preprocess_text(__A						)
      __lowerCamelCase					      =			self.sp_model.encode(__A							,    out_type=__A						)
      __lowerCamelCase					      =			[]
      for piece in pieces:
          if len(__A						) > 1 and piece[-1] == str(''','''						) and piece[-2].isdigit():
              __lowerCamelCase					      =			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:
                      __lowerCamelCase					      =			cur_pieces[1:]
                  else:
                      __lowerCamelCase					      =			cur_pieces[0][1:]
              cur_pieces.append(piece[-1]						)
              new_pieces.extend(__A						)
          else:
              new_pieces.append(__A						)

      return new_pieces






  def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							Dict:
      return self.sp_model.PieceToId(__A						)






  def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							Optional[Any]:
      return self.sp_model.IdToPiece(__A						)






  def 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							Optional[Any]:
      __lowerCamelCase					      =			[]
      __lowerCamelCase					      =			""
      __lowerCamelCase					      =			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
              __lowerCamelCase					      =			True
              __lowerCamelCase					      =			[]
          else:
              current_sub_tokens.append(__A						)
              __lowerCamelCase					      =			False
      out_string += self.sp_model.decode(__A						)
      return out_string.strip()






  def 				__A (	self	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Any = False							,    SCREAMING_SNAKE_CASE__	:							Any = None							,    SCREAMING_SNAKE_CASE__	:							Dict = True							,    **SCREAMING_SNAKE_CASE__	:							Dict							,    )  ->							str:
      __lowerCamelCase					      =			kwargs.pop('''use_source_tokenizer'''							,    __A						)

      __lowerCamelCase					      =			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
      __lowerCamelCase					      =			[]
      __lowerCamelCase					      =			[]
      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						)						)
                  __lowerCamelCase					      =			[]
              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:
          __lowerCamelCase					      =			re.sub(R'''(<unk>) '''							,    R'''\1'''							,    ''' '''.join(__A						)						)
      else:
          __lowerCamelCase					      =			"".join(__A						)

      __lowerCamelCase					      =			(
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
          __lowerCamelCase					      =			self.clean_up_tokenization(__A						)
          return clean_text
      else:
          return text






  def 				__A (	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Tuple							,    SCREAMING_SNAKE_CASE__	:							List[str] = None						)  ->							List[int]:
      __lowerCamelCase					      =			[self.sep_token_id]
      __lowerCamelCase					      =			[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 				__A (	self	:							Any							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any] = None							,    SCREAMING_SNAKE_CASE__	:							Optional[int] = False						)  ->							List[int]:







      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 				__A (	self	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[int] = None						)  ->							List[int]:
      __lowerCamelCase					      =			[self.sep_token_id]
      __lowerCamelCase					      =			[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 				__A (	self	:							Optional[int]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any] = None						)  ->							Tuple[str]:







      if not os.path.isdir(__A						):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
          return
      __lowerCamelCase					      =			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:
              __lowerCamelCase					      =			self.sp_model.serialized_model_proto()
              fi.write(__A						)

      return (out_vocab_file,)





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			Dataset.from_dict({'''i''': range(17							)}							)
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(__lowerCAmelCase							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[Any]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('''shards_per_node'''	,						[1, 2, 3]							)
def 				__magic_name__				(     __lowerCAmelCase			:		Any							)    ->     Union[str, Any]:
				def gen(__lowerCAmelCase			:		Union[str, Any]							):
								for shard in shards:
												yield from ({"i": i, "shard": shard} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			shards_per_node * world_size
				__lowerCamelCase					      =			{'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(__lowerCAmelCase							)]}
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase	,						gen_kwargs=__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				assert full_ds.n_shards == world_size * shards_per_node
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[int]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)

				__lowerCamelCase					      =			split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle(seed=42							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle()

				__lowerCamelCase					      =			split_dataset_by_node(full_ds.shuffle(seed=42							)	,						rank=0	,						world_size=__lowerCAmelCase							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(full_ds.shuffle()	,						rank=0	,						world_size=__lowerCAmelCase							)




@pytest.mark.parametrize('''streaming'''	,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)







@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''	,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]	,						)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     int:
				__lowerCamelCase					      =			True
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)



