



"""simple docstring"""


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_				     =       get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
				from transformers.models.mbart.modeling_mbart import shift_tokens_right

snake_case_				     =       25_0004
snake_case_				     =       25_0020







@require_sentencepiece
@require_tokenizers
class       A_			(     SCREAMING_SNAKE_CASE_	,   unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							MBartaaTokenizer
						__UpperCamelCase						=							MBartaaTokenizerFast
						__UpperCamelCase						=							True
						__UpperCamelCase						=							True






						def 	UpperCAmelCase__     (					self :Dict					)				->			Dict:
												super().setUp()

												# We have a SentencePiece fixture for testing
												UpperCAmelCase             = MBartaaTokenizer(lowercase_   ,  src_lang='en_XX'   ,  tgt_lang='ro_RO'   ,  keep_accents=lowercase_					)
												tokenizer.save_pretrained(self.tmpdirname					)






						def 	UpperCAmelCase__     (					self :Optional[int]					)				->			Optional[Any]:
												UpperCAmelCase             = '<s>'
												UpperCAmelCase             = 0

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_					)   ,  lowercase_					)
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_					)   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Optional[Any]:
												UpperCAmelCase             = list(self.get_tokenizer().get_vocab().keys()					)

												self.assertEqual(vocab_keys[0]   ,  '<s>'					)
												self.assertEqual(vocab_keys[1]   ,  '<pad>'					)
												self.assertEqual(vocab_keys[-1]   ,  '<mask>'					)
												self.assertEqual(len(lowercase_					)   ,  10_54					)






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Optional[int]:
												self.assertEqual(self.get_tokenizer().vocab_size   ,  10_54					)






						def 	UpperCAmelCase__     (					self :Any					)				->			Dict:
												UpperCAmelCase             = MBartaaTokenizer(lowercase_   ,  src_lang='en_XX'   ,  tgt_lang='ro_RO'   ,  keep_accents=lowercase_					)

												UpperCAmelCase             = tokenizer.tokenize('This is a test'					)
												self.assertListEqual(lowercase_   ,  ['▁This', '▁is', '▁a', '▁t', 'est']					)

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(lowercase_					)   ,  [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,  )

												UpperCAmelCase             = tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
												self.assertListEqual(
												    lowercase_   ,  [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']   ,  )
												UpperCAmelCase             = tokenizer.convert_tokens_to_ids(lowercase_					)
												self.assertListEqual(
												    lowercase_   ,  [
												        value + tokenizer.fairseq_offset
												        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
												    ]   ,  )

												UpperCAmelCase             = tokenizer.convert_ids_to_tokens(lowercase_					)
												self.assertListEqual(
												    lowercase_   ,  [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']   ,  )






						@slow
						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Any:
												# fmt: off
												UpperCAmelCase             = {'input_ids': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=lowercase_   ,  model_name='facebook/mbart-large-50'   ,  revision='d3913889c59cd5c9e456b269c376325eabad57e2'   ,  )






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Dict:
												if not self.test_slow_tokenizer:
																		# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																		return

												UpperCAmelCase             = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
																								UpperCAmelCase             = self.rust_tokenizer_class.from_pretrained(lowercase_   ,  **lowercase_					)
																								UpperCAmelCase             = self.tokenizer_class.from_pretrained(lowercase_   ,  **lowercase_					)

																								UpperCAmelCase             = tempfile.mkdtemp()

																								UpperCAmelCase             = tokenizer_r.save_pretrained(lowercase_					)
																								UpperCAmelCase             = tokenizer_p.save_pretrained(lowercase_					)

																								# Checks it save with the same files + the tokenizer.json file for the fast one
																								self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files					)					)
																								UpperCAmelCase             = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f					)
																								self.assertSequenceEqual(lowercase_   ,  lowercase_					)

																								# Checks everything loads correctly in the same way
																								UpperCAmelCase             = tokenizer_r.from_pretrained(lowercase_					)
																								UpperCAmelCase             = tokenizer_p.from_pretrained(lowercase_					)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(lowercase_   ,  lowercase_					)					)
																														# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																														# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																								shutil.rmtree(lowercase_					)

																								# Save tokenizer rust, legacy_format=True
																								UpperCAmelCase             = tempfile.mkdtemp()

																								UpperCAmelCase             = tokenizer_r.save_pretrained(lowercase_   ,  legacy_format=lowercase_					)
																								UpperCAmelCase             = tokenizer_p.save_pretrained(lowercase_					)

																								# Checks it save with the same files
																								self.assertSequenceEqual(lowercase_   ,  lowercase_					)

																								# Checks everything loads correctly in the same way
																								UpperCAmelCase             = tokenizer_r.from_pretrained(lowercase_					)
																								UpperCAmelCase             = tokenizer_p.from_pretrained(lowercase_					)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(lowercase_   ,  lowercase_					)					)

																								shutil.rmtree(lowercase_					)

																								# Save tokenizer rust, legacy_format=False
																								UpperCAmelCase             = tempfile.mkdtemp()

																								UpperCAmelCase             = tokenizer_r.save_pretrained(lowercase_   ,  legacy_format=lowercase_					)
																								UpperCAmelCase             = tokenizer_p.save_pretrained(lowercase_					)

																								# Checks it saved the tokenizer.json file
																								self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files					)					)

																								# Checks everything loads correctly in the same way
																								UpperCAmelCase             = tokenizer_r.from_pretrained(lowercase_					)
																								UpperCAmelCase             = tokenizer_p.from_pretrained(lowercase_					)

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(lowercase_   ,  lowercase_					)					)

																								shutil.rmtree(lowercase_					)







@require_torch
@require_sentencepiece
@require_tokenizers
class       A_			(     unittest.TestCase				):



						"""simple docstring"""
						__UpperCamelCase						=							"""facebook/mbart-large-50-one-to-many-mmt"""
						__UpperCamelCase						=							[
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						__UpperCamelCase						=							[
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						__UpperCamelCase						=							[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]






						@classmethod
						def 	UpperCAmelCase__     (					cls :int					)				->			Optional[int]:
												UpperCAmelCase             = MBartaaTokenizer.from_pretrained(
												    cls.checkpoint_name   ,  src_lang='en_XX'   ,  tgt_lang='ro_RO'					)
												UpperCAmelCase             = 1
												return cls






						def 	UpperCAmelCase__     (					self :Optional[Any]					)				->			Optional[Any]:
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']   ,  25_00_01					)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']   ,  25_00_04					)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']   ,  25_00_20					)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']   ,  25_00_38					)






						def 	UpperCAmelCase__     (					self :Dict					)				->			Tuple:
												UpperCAmelCase             = self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
												self.assertListEqual(self.expected_src_tokens   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :int					)				->			List[Any]:
												self.assertIn(lowercase_   ,  self.tokenizer.all_special_ids					)
												UpperCAmelCase             = [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
												UpperCAmelCase             = self.tokenizer.decode(lowercase_   ,  skip_special_tokens=lowercase_					)
												UpperCAmelCase             = self.tokenizer.decode(generated_ids[1:]   ,  skip_special_tokens=lowercase_					)
												self.assertEqual(lowercase_   ,  lowercase_					)
												self.assertNotIn(self.tokenizer.eos_token   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			str:
												UpperCAmelCase             = ['this is gunna be a long sentence ' * 20]
												assert isinstance(src_text[0]   ,  lowercase_					)
												UpperCAmelCase             = 10
												UpperCAmelCase             = self.tokenizer(lowercase_   ,  max_length=lowercase_   ,  truncation=lowercase_					).input_ids[0]
												self.assertEqual(ids[0]   ,  lowercase_					)
												self.assertEqual(ids[-1]   ,  2					)
												self.assertEqual(len(lowercase_					)   ,  lowercase_					)






						def 	UpperCAmelCase__     (					self :List[Any]					)				->			List[Any]:
												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']					)   ,  [25_00_53, 25_00_01]					)






						def 	UpperCAmelCase__     (					self :List[str]					)				->			Optional[Any]:
												UpperCAmelCase             = tempfile.mkdtemp()
												UpperCAmelCase             = self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(lowercase_					)
												UpperCAmelCase             = MBartaaTokenizer.from_pretrained(lowercase_					)
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,  lowercase_					)






						@require_torch
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Any:
												UpperCAmelCase             = self.tokenizer(self.src_text   ,  text_target=self.tgt_text   ,  padding=lowercase_   ,  return_tensors='pt'					)
												UpperCAmelCase             = shift_tokens_right(batch['labels']   ,  self.tokenizer.pad_token_id					)

												# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
												assert batch.input_ids[1][0] == EN_CODE
												assert batch.input_ids[1][-1] == 2
												assert batch.labels[1][0] == RO_CODE
												assert batch.labels[1][-1] == 2
												assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]






						@require_torch
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Dict:
												UpperCAmelCase             = self.tokenizer(
												    self.src_text   ,  text_target=self.tgt_text   ,  padding=lowercase_   ,  truncation=lowercase_   ,  max_length=len(self.expected_src_tokens					)   ,  return_tensors='pt'   ,  )
												UpperCAmelCase             = shift_tokens_right(batch['labels']   ,  self.tokenizer.pad_token_id					)

												self.assertIsInstance(lowercase_   ,  lowercase_					)

												self.assertEqual((2, 14)   ,  batch.input_ids.shape					)
												self.assertEqual((2, 14)   ,  batch.attention_mask.shape					)
												UpperCAmelCase             = batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens   ,  lowercase_					)
												self.assertEqual(2   ,  batch.decoder_input_ids[0, 0]					)  # decoder_start_token_id
												# Test that special tokens are reset
												self.assertEqual(self.tokenizer.prefix_tokens   ,  [EN_CODE]					)
												self.assertEqual(self.tokenizer.suffix_tokens   ,  [self.tokenizer.eos_token_id]					)






						def 	UpperCAmelCase__     (					self :Tuple					)				->			Any:
												UpperCAmelCase             = self.tokenizer(self.src_text   ,  padding=lowercase_   ,  truncation=lowercase_   ,  max_length=3   ,  return_tensors='pt'					)
												UpperCAmelCase             = self.tokenizer(
												    text_target=self.tgt_text   ,  padding=lowercase_   ,  truncation=lowercase_   ,  max_length=10   ,  return_tensors='pt'					)
												UpperCAmelCase             = targets['input_ids']
												UpperCAmelCase             = shift_tokens_right(lowercase_   ,  self.tokenizer.pad_token_id					)

												self.assertEqual(batch.input_ids.shape[1]   ,  3					)
												self.assertEqual(batch.decoder_input_ids.shape[1]   ,  10					)






						@require_torch
						def 	UpperCAmelCase__     (					self :Tuple					)				->			Any:
												UpperCAmelCase             = self.tokenizer._build_translation_inputs(
												    'A test'   ,  return_tensors='pt'   ,  src_lang='en_XX'   ,  tgt_lang='ar_AR'					)

												self.assertEqual(
												    nested_simplify(lowercase_					)   ,  {
												        # en_XX, A, test, EOS
												        'input_ids': [[25_00_04, 62, 30_34, 2]],
												        'attention_mask': [[1, 1, 1, 1]],
												        # ar_AR
												        'forced_bos_token_id': 25_00_01,
												    }   ,  )







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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
					import torch


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)
SCREAMING_SNAKE_CASE							:List[str]							 =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							:Dict							 =				{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Optional[int]							,A					: Optional[Any]							,A					: Optional[int]=False							,A					: int=False							,A					: Union[str, Any]=False							,A					: int=None							,A					: Optional[Any]=None							,A					: Union[str, Any]=None							,A					: Optional[Any]=None							,A					: Optional[Dict[str, Any]] = None							,**A					: Tuple							,):
					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					__A   			= kwargs.get("name_or_path" )
					if name_or_path is None:
									logger.warning(
									    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
									    " you are testing the model, this can safely be ignored" )
									__A   			= "None"

					# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
					__A   			= "<|endoftext|>" if eos_token is None else eos_token
					__A   			= "<unk>" if unk_token is None else unk_token
					if "gpt-sw3-7b" in name_or_path:
									__A   			= unk_token if pad_token is None else pad_token
									__A   			= eos_token if bos_token is None else bos_token
					else:
									__A   			= "<pad>" if pad_token is None else pad_token
									__A   			= "<s>" if bos_token is None else bos_token

					super().__init__(
					    do_lower_case=A							,remove_space=A							,keep_accents=A							,bos_token=A							,eos_token=A							,unk_token=A							,pad_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A   			= do_lower_case
					__A   			= remove_space
					__A   			= keep_accents
					__A   			= vocab_file

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(A )

					# Used for whitespace normalization in input texts
					# fmt : off
					__A   			= {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
					# fmt : on

					# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
					__A   			= re.compile(
					    f'''[{''.join(map(A							,list(range(0							,9 ) ) + list(range(11							,32 ) ) + list(range(1_27							,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )






	def __getstate__(       self					: Optional[int] ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Optional[Any]							,A					: Union[str, Any] ):
					__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 )






	@property
	# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
	def 				UpperCamelCase_  (       self					: List[str] ):
					return len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int							,A					: str ):
					__A   			= self.non_printing_characters_re.sub(""							,A )

					# Normalize whitespaces
					__A   			= "".join([char if char not in self.whitespaces else " " for char in text] )

					# NFC Unicode normalization
					__A   			= unicodedata.normalize("NFC"							,A )
					return text






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: str							,**A					: Optional[int] ):
					__A   			= self.preprocess_text(A )
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: int ):
					return self.sp_model.IdToPiece(A )






	@staticmethod
	def 				UpperCamelCase_  (       A					: str ):
					return out_string






	def 				UpperCamelCase_  (       self					: str							,A					: List[str] ):
					__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:
													# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
													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






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: List[str]							,A					: str							,A					: Optional[str] = None ):
					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,)






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Union[str, List[str]]							,A					: Union[str, bool] = False ):

					if isinstance(A							,A ):
									__A   			= self.preprocess_text(A )
									__A   			= self.sp_model.encode(A )
					else:
									__A   			= [self.preprocess_text(A ) for t in text]
									__A   			= self.sp_model.encode(A )

					if return_tensors is True or return_tensors == "pt":
									__A   			= torch.tensor(A )

					return token_ids






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Union[int, List[int]] ):
					return self.sp_model.decode(A )







	def 				UpperCamelCase_  (       self					: List[str]							,A					: "Conversation" ):
					__A   			= [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
					__A   			= (
					    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
					)
					return self.encode(text=A )
