






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

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE__		:							str 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							Tuple 				=				{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE__		:							Any 				=				{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

SCREAMING_SNAKE_CASE__		:							int 				=				{
    """moussaKam/mbarthez""": 10_24,
    """moussaKam/barthez""": 10_24,
    """moussaKam/barthez-orangesum-title""": 10_24,
}

SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				"""▁"""







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  VOCAB_FILES_NAMES
     __lowerCamelCase      				=  PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase      				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase      				=  ['input_ids', 'attention_mask']




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase="<s>"	,						_lowerCAmelCase="</s>"	,						_lowerCAmelCase="</s>"	,						_lowerCAmelCase="<s>"	,						_lowerCAmelCase="<unk>"	,						_lowerCAmelCase="<pad>"	,						_lowerCAmelCase="<mask>"	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase__      :  str				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else mask_token

          UpperCAmelCase__      :  Union[str, Any]				      =			{} if sp_model_kwargs is None else sp_model_kwargs

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

          UpperCAmelCase__      :  int				      =			vocab_file
          UpperCAmelCase__      :  Union[str, Any]				      =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(str(_lowerCAmelCase		)		)

          UpperCAmelCase__      :  Dict				      =			{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

          UpperCAmelCase__      :  Union[str, Any]				      =			len(self.sp_model		) - 1
          UpperCAmelCase__      :  Optional[int]				      =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):

          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase__      :  int				      =			[self.cls_token_id]
          UpperCAmelCase__      :  int				      =			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = False		):
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_lowerCAmelCase	,						token_ids_a=_lowerCAmelCase	,						already_has_special_tokens=_lowerCAmelCase		)

          if token_ids_a is None:
               return [1] + ([0] * len(_lowerCAmelCase		)) + [1]
          return [1] + ([0] * len(_lowerCAmelCase		)) + [1, 1] + ([0] * len(_lowerCAmelCase		)) + [1]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  int				      =			[self.sep_token_id]
          UpperCAmelCase__      :  Optional[Any]				      =			[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]




     @property
     def   __UpperCAmelCase  (				self		):
          return len(self.sp_model		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[str]				      =			{self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return self.sp_model.encode(_lowerCAmelCase	,						out_type=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__      :  int				      =			self.sp_model.PieceToId(_lowerCAmelCase		)

          return spm_id if spm_id else self.unk_token_id




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			[]
          UpperCAmelCase__      :  List[str]				      =			""""""
          UpperCAmelCase__      :  Optional[Any]				      =			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(_lowerCAmelCase		) + token
                    UpperCAmelCase__      :  Dict				      =			True
                    UpperCAmelCase__      :  Tuple				      =			[]
               else:
                    current_sub_tokens.append(_lowerCAmelCase		)
                    UpperCAmelCase__      :  Optional[int]				      =			False
          out_string += self.sp_model.decode(_lowerCAmelCase		)
          return out_string.strip()




     def __getstate__(				self		):
          UpperCAmelCase__      :  str				      =			self.__dict__.copy()
          UpperCAmelCase__      :  List[Any]				      =			None
          return state




     def __setstate__(				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			d

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

          UpperCAmelCase__      :  Optional[Any]				      =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(self.vocab_file		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          if not os.path.isdir(_lowerCAmelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          UpperCAmelCase__      :  Tuple				      =			os.path.join(
              _lowerCAmelCase	,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

          if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		) and os.path.isfile(self.vocab_file		):
               copyfile(self.vocab_file	,						_lowerCAmelCase		)
          elif not os.path.isfile(self.vocab_file		):
               with open(_lowerCAmelCase	,						"""wb"""		) as fi:
                    UpperCAmelCase__      :  str				      =			self.sp_model.serialized_model_proto()
                    fi.write(_lowerCAmelCase		)

          return (out_vocab_file,)













'''simple docstring'''




import os
import sys
import unittest


__UpperCAmelCase            =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase            =       os.path.join(git_repo_path, """src""", """transformers""")

__UpperCAmelCase            =       """
{0} = None
"""

__UpperCAmelCase            =       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__UpperCAmelCase            =       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""						)
							self.assertIsNone(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			find_backend("""    if not is_tokenizers_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend("""    if not is_tensorflow_text_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				List[str]		=			find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers_and_vision"""						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("""torch"""      ,						lowerCamelCase_						)
							self.assertIn("""tensorflow_text"""      ,						lowerCamelCase_						)
							self.assertIn("""sentencepiece_and_tokenizers"""      ,						lowerCamelCase_						)

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertModel"""      ,						objects["""tf"""]						)
							self.assertIn("""FlaxBertModel"""      ,						objects["""flax"""]						)
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertTokenizer"""      ,						objects["""tensorflow_text"""]						)
							self.assertIn("""convert_slow_tokenizer"""      ,						objects["""sentencepiece_and_tokenizers"""]						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			create_dummy_object("""CONSTANT"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						"""\nCONSTANT = None\n"""						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_object("""function"""      ,						"""'torch'"""						)
							self.assertEqual(
							    lowerCamelCase_      ,						"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""						)

							SCREAMING_SNAKE_CASE :				str		=			"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			create_dummy_object("""FakeClass"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}						)
							self.assertEqual(dummy_files["""torch"""]      ,						lowerCamelCase_						)



