
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


__lowerCAmelCase      :					List[str]   	=       logging.get_logger(__name__)
__lowerCAmelCase      :					Any   	=       {'vocab_file': 'spiece.model'}

__lowerCAmelCase      :					Optional[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',
    },
}

__lowerCAmelCase      :					Optional[Any]   	=       {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

__lowerCAmelCase      :					int   	=       '▁'




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		VOCAB_FILES_NAMES
  a__   	=		PRETRAINED_VOCAB_FILES_MAP
  a__   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__   	=		["""input_ids""", """token_type_ids"""]
  def __init__(			self      :			Optional[Any]      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			Optional[Any]=False      ,							UpperCamelCase__      :			Dict=True      ,							UpperCamelCase__      :			Union[str, Any]=True      ,							UpperCamelCase__      :			List[Any]="<unk>"      ,							UpperCamelCase__      :			Union[str, Any]="[SEP]"      ,							UpperCamelCase__      :			Dict="<pad>"      ,							UpperCamelCase__      :			int="[CLS]"      ,							UpperCamelCase__      :			int="[MASK]"      ,							UpperCamelCase__      :			Optional[Dict[str, Any]] = None      ,							**UpperCamelCase__      :			List[Any]      ,							)      ->     None:







    """simple docstring"""






    __magic_name__    					=	(
        AddedToken(UpperCamelCase__      ,							lstrip=UpperCamelCase__      ,							rstrip=UpperCamelCase__      ,							normalized=UpperCamelCase__   )
        if isinstance(UpperCamelCase__      ,							UpperCamelCase__   )
        else mask_token
    )

    __magic_name__    					=	{} if sp_model_kwargs is None else sp_model_kwargs

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

    __magic_name__    					=	do_lower_case
    __magic_name__    					=	remove_space
    __magic_name__    					=	keep_accents
    __magic_name__    					=	vocab_file

    __magic_name__    					=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
    self.sp_model.Load(UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[Any]:







    """simple docstring"""






    return len(self.sp_model   )
  def 							_lowercase							(			self      :			Optional[int]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	{self.convert_ids_to_tokens(UpperCamelCase__   ): i for i in range(self.vocab_size   )}
    vocab.update(self.added_tokens_encoder   )
    return vocab
  def __getstate__(			self      :			Any   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	self.__dict__.copy()
    __magic_name__    					=	None
    return state
  def __setstate__(			self      :			Tuple      ,							UpperCamelCase__      :			int   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	d

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

    __magic_name__    					=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
    self.sp_model.Load(self.vocab_file   )
  def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			List[str]   )      ->     Union[str, Any]:







    """simple docstring"""






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

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

    return outputs
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			str   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	self.preprocess_text(UpperCamelCase__   )
    __magic_name__    					=	self.sp_model.encode(UpperCamelCase__      ,							out_type=UpperCamelCase__   )
    __magic_name__    					=	[]
    for piece in pieces:
      if len(UpperCamelCase__   ) > 1 and piece[-1] == str(""","""   ) and piece[-2].isdigit():
        __magic_name__    					=	self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__      ,							""""""   )   )
        if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
          if len(cur_pieces[0]   ) == 1:
            __magic_name__    					=	cur_pieces[1:]
          else:
            __magic_name__    					=	cur_pieces[0][1:]
        cur_pieces.append(piece[-1]   )
        new_pieces.extend(UpperCamelCase__   )
      else:
        new_pieces.append(UpperCamelCase__   )

    return new_pieces
  def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			Optional[int]   )      ->     Optional[int]:







    """simple docstring"""






    return self.sp_model.PieceToId(UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			List[Any]   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.sp_model.IdToPiece(UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			int   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	[]
    __magic_name__    					=	""""""
    __magic_name__    					=	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(UpperCamelCase__   ) + token
        __magic_name__    					=	True
        __magic_name__    					=	[]
      else:
        current_sub_tokens.append(UpperCamelCase__   )
        __magic_name__    					=	False
    out_string += self.sp_model.decode(UpperCamelCase__   )
    return out_string.strip()
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			List[int]      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = None      ,							UpperCamelCase__      :			bool = True      ,							**UpperCamelCase__      :			Optional[Any]      ,							)      ->     str:







    """simple docstring"""






    __magic_name__    					=	kwargs.pop("""use_source_tokenizer"""      ,							UpperCamelCase__   )

    __magic_name__    					=	self.convert_ids_to_tokens(UpperCamelCase__      ,							skip_special_tokens=UpperCamelCase__   )

    # 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
    __magic_name__    					=	[]
    __magic_name__    					=	[]
    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(UpperCamelCase__   )   )
          __magic_name__    					=	[]
        sub_texts.append(UpperCamelCase__   )
      else:
        current_sub_text.append(UpperCamelCase__   )
    if current_sub_text:
      sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__   )   )

    # Mimic the behavior of the Rust tokenizer:
    # No space after <unk>
    if spaces_between_special_tokens:
      __magic_name__    					=	re.sub(R"""(<unk>) """      ,							R"""\1"""      ,							""" """.join(UpperCamelCase__   )   )
    else:
      __magic_name__    					=	"""""".join(UpperCamelCase__   )

    __magic_name__    					=	(
        clean_up_tokenization_spaces
        if clean_up_tokenization_spaces is not None
        else self.clean_up_tokenization_spaces
    )
    if clean_up_tokenization_spaces:
      __magic_name__    					=	self.clean_up_tokenization(UpperCamelCase__   )
      return clean_text
    else:
      return text
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			List[int]      ,							UpperCamelCase__      :			Optional[List[int]] = None   )      ->     List[int]:







    """simple docstring"""






    __magic_name__    					=	[self.sep_token_id]
    __magic_name__    					=	[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 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			List[int]      ,							UpperCamelCase__      :			Optional[List[int]] = None      ,							UpperCamelCase__      :			bool = False   )      ->     List[int]:







    """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 not None:
      return [1] + ([0] * len(UpperCamelCase__   )) + [1] + ([0] * len(UpperCamelCase__   )) + [1]
    return [1] + ([0] * len(UpperCamelCase__   )) + [1]
  def 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			List[int]      ,							UpperCamelCase__      :			Optional[List[int]] = None   )      ->     List[int]:







    """simple docstring"""






    __magic_name__    					=	[self.sep_token_id]
    __magic_name__    					=	[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 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Optional[str] = None   )      ->     Tuple[str]:







    """simple docstring"""






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

    return (out_vocab_file,)



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	copy.deepcopy(A_ )
  __magic_name__    					=	sorted(A_ )
  __magic_name__    					=	[bisect.bisect_right(A_,					A_ ) for y in x]
  return quantized

def a__   (	A_,					A_=0 ):






  '''simple docstring'''





  __magic_name__    					=	np.arange(start=3,					stop=A_,					step=4 ).tolist() if k > 0 else [10]
  __magic_name__    					=	_quantize(A_,					A_ )
  # count number of elements per group
  __magic_name__    					=	np.unique(A_,					return_counts=A_ )[1]
  __magic_name__    					=	[0] + bins + [np.inf]
  logger.info("""Using {} as bins for aspect lengths quantization""".format(A_ ) )
  logger.info("""Count of instances per bin: {}""".format(A_ ) )
  return groups





class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  def __init__(			self      :			Dict      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]      ,							UpperCamelCase__      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    if not isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      raise ValueError(
          """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCamelCase__   )   )
    __magic_name__    					=	sampler
    __magic_name__    					=	group_ids
    __magic_name__    					=	batch_size
  def __iter__(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	defaultdict(UpperCamelCase__   )
    __magic_name__    					=	defaultdict(UpperCamelCase__   )

    __magic_name__    					=	0
    for idx in self.sampler:
      __magic_name__    					=	self.group_ids[idx]
      buffer_per_group[group_id].append(UpperCamelCase__   )
      samples_per_group[group_id].append(UpperCamelCase__   )
      if len(buffer_per_group[group_id]   ) == self.batch_size:
        yield buffer_per_group[group_id]  # TODO
        num_batches += 1
        del buffer_per_group[group_id]
      assert len(buffer_per_group[group_id]   ) < self.batch_size

    # now we have run out of elements that satisfy
    # the group criteria, let's return the remaining
    # elements so that the size of the sampler is
    # deterministic
    __magic_name__    					=	len(self   )
    __magic_name__    					=	expected_num_batches - num_batches
    if num_remaining > 0:
      # for the remaining batches, group the batches by similar lengths
      __magic_name__    					=	[]
      for group_id, idxs in sorted(buffer_per_group.items()      ,							key=lambda UpperCamelCase__   : x[0]   ):
        batch_idx.extend(UpperCamelCase__   )
        if len(UpperCamelCase__   ) >= self.batch_size:
          yield batch_idx[: self.batch_size]
          __magic_name__    					=	batch_idx[self.batch_size :]
          num_remaining -= 1
      if len(UpperCamelCase__   ) > 0:
        yield batch_idx
        num_remaining -= 1
    assert num_remaining == 0





  def __len__(			self      :			List[Any]   )      ->     Any:







    """simple docstring"""






    return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size

