

from timeit import timeit

a_     =			{
    'MALAYALAM': True,
    'String': False,
    'rotor': True,
    'level': True,
    'A': True,
    'BB': True,
    'ABC': False,
    'amanaplanacanalpanama': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				0
	SCREAMING_SNAKE_CASE      :					Tuple    =				len(_a) - 1
	while start_i < end_i:
		if s[start_i] == s[end_i]:
			start_i += 1
			end_i -= 1
		else:
			return False
	return True

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					List[str]    =				len(_a) // 2
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				len(_a)

	# We need to traverse till half of the length of string
	# as we can get access of the i'th last element from
	# i'th index.
	# eg: [0,1,2,3,4,5] => 4th index can be accessed
	# with the help of 1st index (i==n-i-1)
	# where n is length of string
	return all(s[i] == s[n - i - 1] for i in range(_a))

def        lowerCamelCase__       (    _a):
	if len(_a) <= 2:
		return True
	if s[0] == s[len(_a) - 1]:
		return is_palindrome_recursive(s[1:-1])
	else:
		return False

def        lowerCamelCase__       (    _a):
	return s == s[::-1]

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				f"all({name}(key) is value for key, value in test_data.items())"
	SCREAMING_SNAKE_CASE      :					Tuple    =				f"from __main__ import test_data, {name}"
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				500000
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				timeit(stmt=_a							,	setup=_a							,	number=_a)
	print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds")


if __name__ == "__main__":
			for key, value in test_data.items():
						assert is_palindrome(key) is is_palindrome_recursive(key)
						assert is_palindrome(key) is is_palindrome_slice(key)
						print(F'''{key:21} {value}''')
			print('a man a plan a canal panama')

			# finished 500,000 runs in 0.46793 seconds
			benchmark_function('is_palindrome_slice')
			# finished 500,000 runs in 0.85234 seconds
			benchmark_function('is_palindrome')
			# finished 500,000 runs in 1.32028 seconds
			benchmark_function('is_palindrome_recursive')
			# finished 500,000 runs in 2.08679 seconds
			benchmark_function('is_palindrome_traversal')

"""simple docstring"""




import os
import re
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


_A			:       List[str]   		=       logging.get_logger(__name__)

_A			:       List[str]   		=       {"""vocab_file""": """spiece.model"""}

_A			:       List[Any]   		=       {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_A			:       str   		=       {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """attention_mask"""]
   __lowerCAmelCase            =				[]
   def __init__(			self	,    _a	,    _a="<unk>"	,    _a="<s>"	,    _a="</s>"	,    _a="<pad>"	,    _a="[SEP]"	,    _a="[MASK]"	,    _a="[CLS]"	,    _a = None	,    **_a	,    ):
          lowercase    :				Dict       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else bos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else eos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else unk_token
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else pad_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else cls_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else sep_token

          # Mask token behave like a normal word, i.e. include the space before it
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else mask_token

          lowercase    :				Optional[int]       		=							{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=_a	,    eos_token=_a	,    unk_token=_a	,    pad_token=_a	,    sep_token=_a	,    mask_token=_a	,    cls_token=_a	,    sp_model_kwargs=self.sp_model_kwargs	,    **_a	,    )

          lowercase    :				Optional[Any]       		=							vocab_file

          lowercase    :				List[str]       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(_a				)
   @property
   def 			__magic_name__			(			self				):
          return self.sp_model.get_piece_size()
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							{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				):
          lowercase    :				Union[str, Any]       		=							self.__dict__.copy()
          lowercase    :				Dict       		=							None
          return state
   def __setstate__(			self	,    _a				):
          lowercase    :				str       		=							d

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

          lowercase    :				Any       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(self.vocab_file				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.encode(_a	,    out_type=_a				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.piece_to_id(_a				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Union[str, Any]       		=							self.sp_model.IdToPiece(_a				)
          return token
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							[]
          lowercase    :				List[Any]       		=							""
          lowercase    :				List[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
                        lowercase    :				int       		=							True
                        lowercase    :				Union[str, Any]       		=							[]
                 else:
                        current_sub_tokens.append(_a				)
                        lowercase    :				int       		=							False
          out_string += self.sp_model.decode(_a				)
          return out_string.strip()
   def 			__magic_name__			(			self	,    _a	,    _a = False	,    _a = None	,    _a = True	,    **_a	,    ):
          lowercase    :				int       		=							kwargs.pop("use_source_tokenizer"	,    _a				)

          lowercase    :				Union[str, Any]       		=							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
          lowercase    :				Any       		=							[]
          lowercase    :				Dict       		=							[]
          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				)				)
                               lowercase    :				int       		=							[]
                        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 before [MASK] and [SEP]
          if spaces_between_special_tokens:
                 lowercase    :				Dict       		=							re.sub(R" (\[(MASK|SEP)\])"	,    R"\1"	,    " ".join(_a				)				)
          else:
                 lowercase    :				Union[str, Any]       		=							"".join(_a				)

          lowercase    :				int       		=							(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
                 lowercase    :				Union[str, Any]       		=							self.clean_up_tokenization(_a				)
                 return clean_text
          else:
                 return text
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				List[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				) 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:
                        lowercase    :				int       		=							self.sp_model.serialized_model_proto()
                        fi.write(_a				)

          return (out_vocab_file,)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowercase    :				List[str]       		=							[self.cls_token_id]
          lowercase    :				Tuple       		=							[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = 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 None:
                 return [1] + ([0] * len(_a				)) + [1]
          return [1] + ([0] * len(_a				)) + [1] + ([0] * len(_a				)) + [1]

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Tuple       		=							[self.sep_token_id]
          lowercase    :				Tuple       		=							[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]


