




'''simple docstring'''

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 						a				(   _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		BarthezTokenizer
				snake_case_      =		BarthezTokenizerFast
				snake_case_      =		True
				snake_case_      =		True


				def   A_ (		self    :					List[str]					):
									super().setUp()

									snake_case_									=				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''					)
									tokenizer.save_pretrained(self.tmpdirname					)
									tokenizer.save_pretrained(self.tmpdirname       ,				legacy_format=lowercase_					)
									snake_case_									=				tokenizer


				def   A_ (		self    :					Optional[Any]					):
									snake_case_									=				'''<pad>'''
									snake_case_									=				1

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


				def   A_ (		self    :					List[Any]					):
									snake_case_									=				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_1122					)


				def   A_ (		self    :					int					):
									self.assertEqual(self.get_tokenizer().vocab_size       ,				10_1122					)


				@require_torch
				def   A_ (		self    :					str					):
									snake_case_									=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									snake_case_									=				[0, 57, 3018, 7_0307, 91, 2]

									snake_case_									=				self.tokenizer(
									    lowercase_       ,				max_length=len(lowercase_					)       ,				padding=lowercase_       ,				truncation=lowercase_       ,				return_tensors='''pt'''					)
									self.assertIsInstance(lowercase_       ,				lowercase_					)

									self.assertEqual((2, 6)       ,				batch.input_ids.shape					)
									self.assertEqual((2, 6)       ,				batch.attention_mask.shape					)
									snake_case_									=				batch.input_ids.tolist()[0]
									self.assertListEqual(lowercase_       ,				lowercase_					)


				def   A_ (		self    :					Tuple					):
									if not self.test_rust_tokenizer:
														return

									snake_case_									=				self.get_tokenizer()
									snake_case_									=				self.get_rust_tokenizer()

									snake_case_									=				'''I was born in 92000, and this is falsé.'''

									snake_case_									=				tokenizer.tokenize(lowercase_					)
									snake_case_									=				rust_tokenizer.tokenize(lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									snake_case_									=				tokenizer.encode(lowercase_       ,				add_special_tokens=lowercase_					)
									snake_case_									=				rust_tokenizer.encode(lowercase_       ,				add_special_tokens=lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)

									snake_case_									=				self.get_rust_tokenizer()
									snake_case_									=				tokenizer.encode(lowercase_					)
									snake_case_									=				rust_tokenizer.encode(lowercase_					)
									self.assertListEqual(lowercase_       ,				lowercase_					)


				@slow
				def   A_ (		self    :					List[Any]					):
									# fmt: off
									snake_case_									=				{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 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], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 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, 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]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									snake_case_									=				[
									    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
									    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
									    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
									    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
									    '''telles que la traduction et la synthèse de texte.''',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=lowercase_       ,				model_name='''moussaKam/mbarthez'''       ,				revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''       ,				sequences=lowercase_       ,				)



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 )
