
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__							=				get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE__							=				get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE__							=				"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class 	A__   (						UpperCAmelCase__    ,						unittest.TestCase				):
							lowerCAmelCase__		:						int    			=							CamembertTokenizer
							lowerCAmelCase__		:						Tuple    			=							CamembertTokenizerFast
							lowerCAmelCase__		:						Optional[Any]    			=							True
							lowerCAmelCase__		:						Optional[Any]    			=							True





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



											"""simple docstring"""


											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowercase								=					CamembertTokenizer(lowercase_				)
											tokenizer.save_pretrained(self.tmpdirname				)





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



											"""simple docstring"""


											__lowercase								=					'''<pad>'''
											__lowercase								=					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  :   Dict				)						->		Any:



											"""simple docstring"""


											__lowercase								=					list(self.get_tokenizer().get_vocab().keys()				)

											self.assertEqual(vocab_keys[0]     , '<s>NOTUSED'				)
											self.assertEqual(vocab_keys[1]     , '<pad>'				)
											self.assertEqual(vocab_keys[-1]     , '<mask>'				)
											self.assertEqual(len(lowercase_				)     , 10_04				)





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



											"""simple docstring"""


											self.assertEqual(self.get_tokenizer().vocab_size     , 10_05				)





							def 			a__ ( self  :   Dict				)						->		List[Any]:



											"""simple docstring"""


											__lowercase								=					CamembertTokenizer(lowercase_				)
											tokenizer.save_pretrained(self.tmpdirname				)
											__lowercase								=					CamembertTokenizerFast.from_pretrained(self.tmpdirname				)

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

											__lowercase								=					tokenizer.encode(lowercase_				)
											__lowercase								=					rust_tokenizer.encode(lowercase_				)
											self.assertListEqual(lowercase_     , lowercase_				)

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

											# <unk> tokens are not the same for `rust` than for `slow`.
											# Because spm gives back raw token instead of `unk` in EncodeAsPieces
											# tokens = tokenizer.tokenize(sequence)
											__lowercase								=					tokenizer.convert_ids_to_tokens(lowercase_				)
											__lowercase								=					rust_tokenizer.tokenize(lowercase_				)
											self.assertListEqual(lowercase_     , lowercase_				)





							def 			a__ ( self  :   int				)						->		int:



											"""simple docstring"""


											if not self.test_rust_tokenizer:
															return

											__lowercase								=					self.get_tokenizer()
											__lowercase								=					self.get_rust_tokenizer()

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

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

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

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





							@slow
							def 			a__ ( self  :   Any				)						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								=					{'''input_ids''': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '''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, 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]]}  # noqa: E501
											# fmt: on

											# camembert is a french model. So we also use french texts.
											__lowercase								=					[
											    '''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='camembert-base'     , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'     , sequences=lowercase_     , )













"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_   : Dict   			= logging.get_logger(__name__)

UpperCAmelCase_   : Tuple   			= """▁"""

UpperCAmelCase_   : Optional[Any]   			= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_   : str   			= {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase_   : str   			= {
    """facebook/xglm-564M""": 2048,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      def __init__(						self     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Tuple="<s>"						,	lowercase_     :  Any="</s>"						,	lowercase_     :  Optional[int]="</s>"						,	lowercase_     :  List[Any]="<s>"						,	lowercase_     :  Union[str, Any]="<unk>"						,	lowercase_     :  Union[str, Any]="<pad>"						,	lowercase_     :  Optional[Dict[str, Any]] = None						,	**lowercase_     :  Tuple						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		7
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[F'<madeupword{i}>' for i in range(self.num_madeup_words)]

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''additional_special_tokens'''						,	[])
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              bos_token=lowercase_						,	eos_token=lowercase_						,	unk_token=lowercase_						,	sep_token=lowercase_						,	cls_token=lowercase_						,	pad_token=lowercase_						,	sp_model_kwargs=self.sp_model_kwargs						,	**lowercase_						,	)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(str(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		1

          # Mimic fairseq token-to-id alignment for the first 4 token
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		len(self.sp_model)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
          self.fairseq_tokens_to_ids.update(lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def __getstate__(						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.__dict__.copy()
          SCREAMING_SNAKE_CASE_      :  str    						=		None
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.sp_model.serialized_model_proto()
          return state
      def __setstate__(						self     :  Tuple						,	lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		d

          # for backward compatibility
          if not hasattr(self						,	'''sp_model_kwargs'''):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None						,	lowercase_     :  bool = False):




          '''simple docstring'''


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

          if token_ids_a is None:
              return [1] + ([0] * len(lowercase_))
          return [1] + ([0] * len(lowercase_)) + [1, 1] + ([0] * len(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[self.sep_token_id]

          if token_ids_a is None:
              return len(sep + token_ids_a) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		{self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  str):




          '''simple docstring'''


          return self.sp_model.encode(lowercase_						,	out_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.sp_model.PieceToId(lowercase_)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]):




          '''simple docstring'''


          if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		''''''.join(lowercase_).replace(lowercase_						,	''' ''').strip()
          return out_string



      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


          if not os.path.isdir(lowercase_):
              logger.error(F'Vocabulary path ({save_directory}) should be a directory')
              return
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		os.path.join(
              lowercase_						,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

          if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_) and os.path.isfile(self.vocab_file):
              copyfile(self.vocab_file						,	lowercase_)
          elif not os.path.isfile(self.vocab_file):
              with open(lowercase_						,	'''wb''') as fi:
                  SCREAMING_SNAKE_CASE_      :  int    						=		self.sp_model.serialized_model_proto()
                  fi.write(lowercase_)

          return (out_vocab_file,)


