



"""simple docstring"""






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
	from ...tokenization_utils_base import TextInput
from ...utils import logging


__snake_case				:  Union[str, Any] =       logging.get_logger(__name__)

__snake_case				:  Any =       {'vocab_file': 'spiece.model'}

__snake_case				:  Optional[Any] =       {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
__snake_case				:  str =       {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

__snake_case				:  Any =       '▁'



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE        =      PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE        =      ['input_ids', 'attention_mask']

	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Tuple="</s>"			,     _SCREAMING_SNAKE_CASE:						Tuple="<unk>"			,     _SCREAMING_SNAKE_CASE:						Optional[int]="<pad>"			,     _SCREAMING_SNAKE_CASE:						Optional[int]=100			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Dict[str, Any]] = None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=True			,     **_SCREAMING_SNAKE_CASE:						Dict			,     )							->			None:

				"""simple docstring"""

				if extra_ids > 0 and additional_special_tokens is None:
							__lowerCAmelCase :    List[str]				=			[F"""<extra_id_{i}>""" for i in range(_SCREAMING_SNAKE_CASE)]
				elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							__lowerCAmelCase :    Union[str, Any]				=			len(set(filter(lambda _SCREAMING_SNAKE_CASE: bool("extra_id" in str(_SCREAMING_SNAKE_CASE))			,     _SCREAMING_SNAKE_CASE)))
							if extra_tokens != extra_ids:
										raise ValueError(
										    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
										    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
										    " tokens")
				if legacy:
							logger.warning_once(
							    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
							    " read the related pull request available at https://github.com/huggingface/transformers/pull/24565")

				__lowerCAmelCase :    str				=			legacy
				__lowerCAmelCase :    List[Any]				=			{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    eos_token=_SCREAMING_SNAKE_CASE			,     unk_token=_SCREAMING_SNAKE_CASE			,     pad_token=_SCREAMING_SNAKE_CASE			,     extra_ids=_SCREAMING_SNAKE_CASE			,     additional_special_tokens=_SCREAMING_SNAKE_CASE			,     sp_model_kwargs=self.sp_model_kwargs			,     legacy=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    Dict				=			vocab_file
				__lowerCAmelCase :    List[str]				=			extra_ids

				__lowerCAmelCase :    Tuple				=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(_SCREAMING_SNAKE_CASE)

	@staticmethod
	def 				_SCREAMING_SNAKE_CASE  (	_SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			Dict:

				"""simple docstring"""

				if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
							__lowerCAmelCase :    Dict				=			TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
							if init_max_model_length is not None and init_max_model_length != max_model_length:
										return init_max_model_length
							elif init_max_model_length is None:
										warnings.warn(
										    "This tokenizer was incorrectly instantiated with a model max length of"
										    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
										    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
										    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
										    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
										    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
										    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
										    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
										    " instantiate this tokenizer with `model_max_length` set to your preferred value."			,     _SCREAMING_SNAKE_CASE			,     )

				return max_model_length

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			int:

				"""simple docstring"""

				return self.sp_model.get_piece_size() + self._extra_ids

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[int]			,     _SCREAMING_SNAKE_CASE:						Optional[List[int]] = None			,     _SCREAMING_SNAKE_CASE:						bool = False)							->			List[int]:

				"""simple docstring"""

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

				# normal case: some special tokens
				if token_ids_a is None:
							return ([0] * len(_SCREAMING_SNAKE_CASE)) + [1]
				return ([0] * len(_SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE)) + [1]

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[int]:

				"""simple docstring"""

				return list(
				    set(filter(lambda _SCREAMING_SNAKE_CASE: bool(re.search(r"<extra_id_\d+>"			,     _SCREAMING_SNAKE_CASE)) is not None			,     self.additional_special_tokens)))

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			Optional[Any]:

				"""simple docstring"""

				return [self._convert_token_to_id(_SCREAMING_SNAKE_CASE) for token in self.get_sentinel_tokens()]

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[int])							->			List[int]:

				"""simple docstring"""

				if len(_SCREAMING_SNAKE_CASE) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
							    " eos tokens being added.")
							return token_ids
				else:
							return token_ids + [self.eos_token_id]

	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						List[int]			,     _SCREAMING_SNAKE_CASE:						Optional[List[int]] = None)							->			List[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			[self.eos_token_id]

				if token_ids_a is None:
							return len(token_ids_a + eos) * [0]
				return len(token_ids_a + eos + token_ids_a + eos) * [0]

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[int]			,     _SCREAMING_SNAKE_CASE:						Optional[List[int]] = None)							->			List[int]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE)
				if token_ids_a is None:
							return token_ids_a
				else:
							__lowerCAmelCase :    Optional[Any]				=			self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE)
							return token_ids_a + token_ids_a

	def __getstate__(	self:						Dict)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.__dict__.copy()
				__lowerCAmelCase :    List[Any]				=			None
				return state

	def __setstate__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			d

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

				__lowerCAmelCase :    Dict				=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						"TextInput"			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			List[str]:

				"""simple docstring"""

				if not self.legacy:
							__lowerCAmelCase :    Optional[Any]				=			SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE			,     " ")
				return super().tokenize(_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     **_SCREAMING_SNAKE_CASE:						str)							->			List[Any]:

				"""simple docstring"""

				if not self.legacy:
							__lowerCAmelCase :    Optional[int]				=			text.startswith(_SCREAMING_SNAKE_CASE)
							if is_first:
										__lowerCAmelCase :    Dict				=			text[1:]

				__lowerCAmelCase :    Optional[int]				=			self.sp_model.encode(_SCREAMING_SNAKE_CASE			,     out_type=_SCREAMING_SNAKE_CASE)

				if not self.legacy and not is_first and not text.startswith(" ") and tokens[0].startswith(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
				return tokens

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						int)							->			Union[str, Any]:

				"""simple docstring"""

				if token.startswith("<extra_id_"):
							__lowerCAmelCase :    Tuple				=			re.match(r"<extra_id_(\d+)>"			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    Any				=			int(match.group(1))
							return self.vocab_size - num - 1
				return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Any)							->			Optional[int]:

				"""simple docstring"""

				if index < self.sp_model.get_piece_size():
							__lowerCAmelCase :    Dict				=			self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase :    Optional[int]				=			F"""<extra_id_{self.vocab_size - 1 - index}>"""
				return token

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			[]
				__lowerCAmelCase :    Optional[int]				=			""
				__lowerCAmelCase :    Optional[int]				=			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(_SCREAMING_SNAKE_CASE) + token
										__lowerCAmelCase :    Dict				=			True
										__lowerCAmelCase :    Optional[int]				=			[]
							else:
										current_sub_tokens.append(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase :    List[str]				=			False
				out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE)
				return out_string.strip()







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Optional[str] = None)							->			Tuple[str]:

				"""simple docstring"""

				if not os.path.isdir(_SCREAMING_SNAKE_CASE):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
							return
				__lowerCAmelCase :    Union[str, Any]				=			os.path.join(
				    _SCREAMING_SNAKE_CASE			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

				if os.path.abspath(self.vocab_file) != os.path.abspath(_SCREAMING_SNAKE_CASE) and os.path.isfile(self.vocab_file):
							copyfile(self.vocab_file			,     _SCREAMING_SNAKE_CASE)
				elif not os.path.isfile(self.vocab_file):
							with open(_SCREAMING_SNAKE_CASE			,     "wb") as fi:
										__lowerCAmelCase :    Optional[Any]				=			self.sp_model.serialized_model_proto()
										fi.write(_SCREAMING_SNAKE_CASE)

				return (out_vocab_file,)




"""simple docstring"""






import argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)