





"""simple docstring"""


import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class    __SCREAMING_SNAKE_CASE			(  tf.keras.layers.Layer      ):







  '''simple docstring'''


  def __init__(						self      :				Dict    ,      __a      :				List    ,      __a      :				bool    ,      __a      :				int = None    ,      __a      :				int = None    ,      __a      :				int = None    ,      __a      :				str = "longest"    ,      __a      :				bool = True    ,      __a      :				int = 512    ,      __a      :				int = None    ,      __a      :				bool = True    ,      __a      :				bool = True    ,      __a      :				bool = True    ,      **__a      :				Any    ,      )						-> Dict:
        super().__init__()
        if use_fast_bert_tokenizer:
              _UpperCamelCase					:		Optional[Any]  =				FastBertTokenizer(
                  __a    ,      token_out_type=tf.intaa    ,      lower_case_nfd_strip_accents=__a    ,      **__a		)
        else:
              _UpperCamelCase					:		Any  =				tf.lookup.StaticVocabularyTable(
                  tf.lookup.KeyValueTensorInitializer(
                      keys=__a    ,      key_dtype=tf.string    ,      values=tf.range(tf.size(__a    ,      out_type=tf.intaa		)    ,      dtype=tf.intaa		)    ,      value_dtype=tf.intaa    ,      )    ,      num_oov_buckets=1    ,      )
              _UpperCamelCase					:		Dict  =				BertTokenizerLayer(
                  __a    ,      token_out_type=tf.intaa    ,      lower_case=__a    ,      **__a		)

        _UpperCamelCase					:		str  =				vocab_list
        _UpperCamelCase					:		int  =				do_lower_case
        _UpperCamelCase					:		Optional[int]  =				cls_token_id or vocab_list.index("[CLS]"		)
        _UpperCamelCase					:		Optional[int]  =				sep_token_id or vocab_list.index("[SEP]"		)
        _UpperCamelCase					:		Dict  =				pad_token_id or vocab_list.index("[PAD]"		)
        _UpperCamelCase					:		List[Any]  =				ShrinkLongestTrimmer(max_length - 3    ,      axis=1		)  # Allow room for special tokens
        _UpperCamelCase					:		Union[str, Any]  =				max_length
        _UpperCamelCase					:		int  =				padding
        _UpperCamelCase					:		Any  =				truncation
        _UpperCamelCase					:		Any  =				pad_to_multiple_of
        _UpperCamelCase					:		Optional[int]  =				return_token_type_ids
        _UpperCamelCase					:		Any  =				return_attention_mask

  @classmethod
  def       __SCREAMING_SNAKE_CASE (						cls      :				Tuple    ,      __a      :				"PreTrainedTokenizerBase"    ,      **__a      :				int		)						-> Dict:  # noqa: F821
        _UpperCamelCase					:		List[str]  =				kwargs.pop("do_lower_case"    ,      __a		)
        _UpperCamelCase					:		Dict  =				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        _UpperCamelCase					:		int  =				kwargs.pop("cls_token_id"    ,      __a		)
        _UpperCamelCase					:		List[Any]  =				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        _UpperCamelCase					:		str  =				kwargs.pop("sep_token_id"    ,      __a		)
        _UpperCamelCase					:		List[Any]  =				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        _UpperCamelCase					:		Dict  =				kwargs.pop("pad_token_id"    ,      __a		)
        _UpperCamelCase					:		Optional[Any]  =				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        _UpperCamelCase					:		Union[str, Any]  =				tokenizer.get_vocab()
        _UpperCamelCase					:		int  =				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]    ,      key=lambda __a		: x[1]		)
        _UpperCamelCase					:		Optional[int]  =				[entry[0] for entry in vocab]
        return cls(
            vocab_list=__a    ,      do_lower_case=__a    ,      cls_token_id=__a    ,      sep_token_id=__a    ,      pad_token_id=__a    ,      **__a    ,      )

  @classmethod
  def       __SCREAMING_SNAKE_CASE (						cls      :				List[Any]    ,      __a      :				Union[str, os.PathLike]    ,      *__a      :				int    ,      **__a      :				Any		)						-> Dict:
        try:
              _UpperCamelCase					:		Dict  =				BertTokenizer.from_pretrained(__a    ,      *__a    ,      **__a		)
        except:  # noqa: E722
              from .tokenization_bert_fast import BertTokenizerFast

              _UpperCamelCase					:		str  =				BertTokenizerFast.from_pretrained(__a    ,      *__a    ,      **__a		)
        return cls.from_tokenizer(__a    ,      **__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				Optional[Any]		)						-> List[str]:
        if self.do_lower_case:
              _UpperCamelCase					:		Tuple  =				case_fold_utfa(__a		)
        _UpperCamelCase					:		Optional[int]  =				self.tf_tokenizer.tokenize(__a		)
        return tokens.merge_dims(1    ,      -1		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				int    ,      __a      :				int=None    ,      __a      :				Any=None    ,      __a      :				Dict=None    ,      __a      :				List[str]=None    ,      __a      :				List[str]=None    ,      __a      :				str=None    ,      __a      :				Optional[Any]=None    ,      )						-> Union[str, Any]:
        if padding is None:
              _UpperCamelCase					:		Dict  =				self.padding
        if padding not in ("longest", "max_length"):
              raise ValueError("Padding must be either 'longest' or 'max_length'!"		)
        if max_length is not None and text_pair is not None:
              # Because we have to instantiate a Trimmer to do it properly
              raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"		)
        if max_length is None:
              _UpperCamelCase					:		List[str]  =				self.max_length
        if truncation is None:
              _UpperCamelCase					:		Dict  =				self.truncation
        if pad_to_multiple_of is None:
              _UpperCamelCase					:		List[Any]  =				self.pad_to_multiple_of
        if return_token_type_ids is None:
              _UpperCamelCase					:		int  =				self.return_token_type_ids
        if return_attention_mask is None:
              _UpperCamelCase					:		Any  =				self.return_attention_mask
        if not isinstance(__a    ,      tf.Tensor		):
              _UpperCamelCase					:		int  =				tf.convert_to_tensor(__a		)
        if text_pair is not None and not isinstance(__a    ,      tf.Tensor		):
              _UpperCamelCase					:		Optional[Any]  =				tf.convert_to_tensor(__a		)
        if text_pair is not None:
              if text.shape.rank > 1:
                    raise ValueError("text argument should not be multidimensional when a text pair is supplied!"		)
              if text_pair.shape.rank > 1:
                    raise ValueError("text_pair should not be multidimensional!"		)
        if text.shape.rank == 2:
              _UpperCamelCase,     _UpperCamelCase					:		str  =				text[:, 0], text[:, 1]
        _UpperCamelCase					:		Tuple  =				self.unpaired_tokenize(__a		)
        if text_pair is None:  # Unpaired text
              if truncation:
                    _UpperCamelCase					:		Tuple  =				text[:, : max_length - 2]  # Allow room for special tokens
              _UpperCamelCase,     _UpperCamelCase					:		int  =				combine_segments(
                  (text,)    ,      start_of_sequence_id=self.cls_token_id    ,      end_of_segment_id=self.sep_token_id		)
        else:  # Paired text
              _UpperCamelCase					:		int  =				self.unpaired_tokenize(__a		)
              if truncation:
                    _UpperCamelCase,     _UpperCamelCase					:		Any  =				self.paired_trimmer.trim([text, text_pair]		)
              _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				combine_segments(
                  (text, text_pair)    ,      start_of_sequence_id=self.cls_token_id    ,      end_of_segment_id=self.sep_token_id		)
        if padding == "longest":
              _UpperCamelCase					:		Optional[Any]  =				input_ids.bounding_shape(axis=1		)
              if pad_to_multiple_of is not None:
                    # No ceiling division in tensorflow, so we negate floordiv instead
                    _UpperCamelCase					:		Union[str, Any]  =				pad_to_multiple_of * (-tf.math.floordiv(-pad_length    ,      __a		))
        else:
              _UpperCamelCase					:		Optional[Any]  =				max_length

        _UpperCamelCase,     _UpperCamelCase					:		Union[str, Any]  =				pad_model_inputs(__a    ,      max_seq_length=__a    ,      pad_value=self.pad_token_id		)
        _UpperCamelCase					:		str  =				{"input_ids": input_ids}
        if return_attention_mask:
              _UpperCamelCase					:		Union[str, Any]  =				attention_mask
        if return_token_type_ids:
              _UpperCamelCase,     _UpperCamelCase					:		Union[str, Any]  =				pad_model_inputs(
                  __a    ,      max_seq_length=__a    ,      pad_value=self.pad_token_id		)
              _UpperCamelCase					:		Any  =				token_type_ids
        return output


  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Dict:
        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }







"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)


lowerCamelCase__      =      "▁"

lowerCamelCase__      =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__      =      {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

lowerCamelCase__      =      {
    "google/reformer-crime-and-punishment": 52_4288,
}



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:Any		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:str		   =    ReformerTokenizer

  def __init__(						self      :				List[str]    ,      __a      :				str=None    ,      __a      :				List[Any]=None    ,      __a      :				Optional[int]="</s>"    ,      __a      :				str="<unk>"    ,      __a      :				Dict=[]    ,      **__a      :				Tuple    ,      )						-> Optional[int]:
        super().__init__(
            __a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      unk_token=__a    ,      additional_special_tokens=__a    ,      **__a    ,      )

        _UpperCamelCase					:		Dict  =				vocab_file
        _UpperCamelCase					:		Optional[Any]  =				False if not self.vocab_file else True


  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."		)

        if not os.path.isdir(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		Optional[Any]  =				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		):
              copyfile(self.vocab_file    ,      __a		)

        return (out_vocab_file,)
