
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


__a         =   logging.get_logger(__name__)

__a         =   {'vocab_file': 'spiece.model'}

__a         =   {
    '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
__a         =   {
    't5-small': 5_1_2,
    't5-base': 5_1_2,
    't5-large': 5_1_2,
    't5-3b': 5_1_2,
    't5-11b': 5_1_2,
}

__a         =   '▁'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      VOCAB_FILES_NAMES
	a     :Tuple       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :int       	=      ['input_ids', 'attention_mask']
	def __init__(			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     str="</s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="<unk>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="<pad>"			,				SCREAMING_SNAKE_CASE_    :     int=1_0_0			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Optional[Dict[str, Any]] = None			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	None:
					# Add extra_ids to the special token list
					if extra_ids > 0 and additional_special_tokens is None:
									lowercase_	    =   [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
									lowercase_	    =   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'''			)

					lowercase_	    =   legacy
					lowercase_	    =   {} 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_			,				)

					lowercase_	    =   vocab_file
					lowercase_	    =   extra_ids

					lowercase_	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(SCREAMING_SNAKE_CASE_			)
	@staticmethod
	def 		_lowercase    (			SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
									lowercase_	    =   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 		_lowercase    (			self    :     Dict			)			->	Tuple:
					return self.sp_model.get_piece_size() + self._extra_ids
	def 		_lowercase    (			self    :     int			)			->	Optional[Any]:
					lowercase_	    =   {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 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			,				SCREAMING_SNAKE_CASE_    :     bool = False			)			->	List[int]:
					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 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					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 		_lowercase    (			self    :     int			)			->	str:
					return [self._convert_token_to_id(SCREAMING_SNAKE_CASE_			) for token in self.get_sentinel_tokens()]
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			)			->	List[int]:
					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 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   [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 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   self._add_eos_if_not_present(SCREAMING_SNAKE_CASE_			)
					if token_ids_a is None:
									return token_ids_a
					else:
									lowercase_	    =   self._add_eos_if_not_present(SCREAMING_SNAKE_CASE_			)
									return token_ids_a + token_ids_a
	def __getstate__(			self    :     Optional[int]			)			->	int:
					lowercase_	    =   self.__dict__.copy()
					lowercase_	    =   None
					return state
	def __setstate__(			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	Tuple:
					lowercase_	    =   d

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

					lowercase_	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(self.vocab_file			)
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     "TextInput"			,				**SCREAMING_SNAKE_CASE_    :     int			)			->	List[str]:
					# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
					# the beginning of the text
					if not self.legacy:
									lowercase_	    =   SPIECE_UNDERLINE + text.replace(SCREAMING_SNAKE_CASE_			,				''' '''			)
					return super().tokenize(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				**SCREAMING_SNAKE_CASE_    :     Any			)			->	Tuple:
					if not self.legacy:
									lowercase_	    =   text.startswith(SCREAMING_SNAKE_CASE_			)
									if is_first:
													lowercase_	    =   text[1:]

					lowercase_	    =   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_			):
									lowercase_	    =   ([tokens[0][1:]] if len(tokens[0]			) > 1 else []) + tokens[1:]
					return tokens
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	List[str]:
					if token.startswith('''<extra_id_'''			):
									lowercase_	    =   re.match(R'''<extra_id_(\d+)>'''			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   int(match.group(1			)			)
									return self.vocab_size - num - 1
					return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	Optional[Any]:
					if index < self.sp_model.get_piece_size():
									lowercase_	    =   self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_			)
					else:
									lowercase_	    =   f'''<extra_id_{self.vocab_size - 1 - index}>'''
					return token
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Optional[int]:
					lowercase_	    =   []
					lowercase_	    =   ''''''
					lowercase_	    =   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
													lowercase_	    =   True
													lowercase_	    =   []
									else:
													current_sub_tokens.append(SCREAMING_SNAKE_CASE_			)
													lowercase_	    =   False
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_			)
					return out_string.strip()




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   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:
													lowercase_	    =   self.sp_model.serialized_model_proto()
													fi.write(SCREAMING_SNAKE_CASE_			)

					return (out_vocab_file,)






from __future__ import annotations

__a         =   8.988E9  # units = N * m^s * C^-2




def 							a		(				snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float		,  snake_case__:   float   ):


				'''simple docstring'''
				lowercase_	    =   abs(chargea * chargea   )

				if (force, chargea, chargea, distance).count(0   ) != 1:
								raise ValueError('''One and only one argument must be 0'''   )
				if distance < 0:
								raise ValueError('''Distance cannot be negative'''   )
				if force == 0:
								lowercase_	    =   COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								lowercase_	    =   abs(snake_case__   ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								lowercase_	    =   (COULOMBS_CONSTANT * charge_product / abs(snake_case__   )) ** 0.5
								return {"distance": distance}
				raise ValueError('''Exactly one argument must be 0'''   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




