


"""simple docstring"""


from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters






def      a__ (				)	->	Tuple:
				_A					  =			{
				    "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
				    "path": ["test_1.py", "test_2.py", "unit_test.py"],
				    "content": ["a " * 20, "a " * 30, "b " * 7],
				}
				_A					  =			Dataset.from_dict(__lowercase   )
				return dataset




class snake_case      (  _UpperCamelCase):

		'''simple docstring'''





		def  a_ (					self      :       Any		)       ->						Any:





						'''simple docstring'''







						_A					  =			get_dataset()
						_A					  =			make_duplicate_clusters(a__    ,   0.8_5		)
						self.assertEqual(len(duplicate_clusters[0]		)    ,   2		)







		def  a_ (					self      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			get_dataset()
						_A  ,		_A					  =			deduplicate_dataset(a__		)
						self.assertEqual(len(a__		)    ,   2		)
						print(a__		)
						self.assertEqual(duplicate_clusters[0][0]["copies"]    ,   2		)
						self.assertEqual(duplicate_clusters[0][0]["is_extreme"]    ,   a__		)



"""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


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_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

a_       							=    "▁"
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			['input_ids', 'attention_mask']







		def __init__(					self      :       List[str]    ,   a__      :       Optional[int]    ,   a__      :       Union[str, Any]="</s>"    ,   a__      :       Union[str, Any]="<unk>"    ,   a__      :       str="<pad>"    ,   a__      :       Optional[int]=1_00    ,   a__      :       List[Any]=None    ,   a__      :       Optional[Dict[str, Any]] = None    ,   a__      :       Any=True    ,   **a__      :       Optional[int]    ,   )       ->						None:





						'''simple docstring'''







						if extra_ids > 0 and additional_special_tokens is None:
										_A					  =			[F"""<extra_id_{i}>""" for i in range(a__		)]
						elif extra_ids > 0 and additional_special_tokens is not None:
										# Check that we have the right number of extra_id special tokens
										_A					  =			len(set(filter(lambda a__		: bool("extra_id" in str(a__		)		)    ,   a__		)		)		)
										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"		)

						_A					  =			legacy
						_A					  =			{} if sp_model_kwargs is None else sp_model_kwargs

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

						_A					  =			vocab_file
						_A					  =			extra_ids

						_A					  =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(a__		)







		@staticmethod
		def  a_ (					a__      :       List[str]    ,   a__      :       Optional[int]    ,   a__      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
										_A					  =			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."    ,   a__    ,   )

						return max_model_length







		@property
		def  a_ (					self      :       List[Any]		)       ->						Dict:





						'''simple docstring'''







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







		def  a_ (					self      :       Dict		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			{self.convert_ids_to_tokens(a__		): i for i in range(self.vocab_size		)}
						vocab.update(self.added_tokens_encoder		)
						return vocab







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None    ,   a__      :       bool = False		)       ->						List[int]:





						'''simple docstring'''







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

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







		def  a_ (					self      :       List[str]		)       ->						List[str]:





						'''simple docstring'''







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







		def  a_ (					self      :       str		)       ->						List[Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]    ,   a__      :       List[int]		)       ->						List[int]:





						'''simple docstring'''







						if len(a__		) > 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  a_ (					self      :       int    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







						_A					  =			[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  a_ (					self      :       Union[str, Any]    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







						_A					  =			self._add_eos_if_not_present(a__		)
						if token_ids_a is None:
										return token_ids_a
						else:
										_A					  =			self._add_eos_if_not_present(a__		)
										return token_ids_a + token_ids_a







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





						'''simple docstring'''







						_A					  =			self.__dict__.copy()
						_A					  =			None
						return state







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





						'''simple docstring'''







						_A					  =			d

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

						_A					  =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(self.vocab_file		)







		def  a_ (					self      :       int    ,   a__      :       "TextInput"    ,   **a__      :       List[str]		)       ->						List[str]:





						'''simple docstring'''







						if not self.legacy:
										_A					  =			SPIECE_UNDERLINE + text.replace(a__    ,   " "		)
						return super().tokenize(a__    ,   **a__		)







		def  a_ (					self      :       str    ,   a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						Any:





						'''simple docstring'''







						if not self.legacy:
										_A					  =			text.startswith(a__		)
										if is_first:
														_A					  =			text[1:]

						_A					  =			self.sp_model.encode(a__    ,   out_type=a__		)

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







		def  a_ (					self      :       int    ,   a__      :       List[Any]		)       ->						List[str]:





						'''simple docstring'''







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







		def  a_ (					self      :       Dict    ,   a__      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







						if index < self.sp_model.get_piece_size():
										_A					  =			self.sp_model.IdToPiece(a__		)
						else:
										_A					  =			F"""<extra_id_{self.vocab_size - 1 - index}>"""
						return token







		def  a_ (					self      :       Optional[int]    ,   a__      :       Tuple		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			[]
						_A					  =			""
						_A					  =			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
														_A					  =			True
														_A					  =			[]
										else:
														current_sub_tokens.append(a__		)
														_A					  =			False
						out_string += self.sp_model.decode(a__		)
						return out_string.strip()







		def  a_ (					self      :       Dict    ,   a__      :       str    ,   a__      :       Optional[str] = None		)       ->						Tuple[str]:





						'''simple docstring'''







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

						return (out_vocab_file,)