



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


a_						:     List[Any]	     =		logging.get_logger(__name__)

a_						:     int	     =		{"""vocab_file""": """spiece.model"""}

a_						:     Any	     =		{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

a_						:     int	     =		{"""bert_for_seq_generation""": 5_1_2}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



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







     def __init__(self				,			__a				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<pad>"				,			__a="<::::>"				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           # Add extra_ids to the special token list
           super().__init__(
               bos_token=UpperCamelCase__				,			eos_token=UpperCamelCase__				,			unk_token=UpperCamelCase__				,			pad_token=UpperCamelCase__				,			sep_token=UpperCamelCase__				,			sp_model_kwargs=self.sp_model_kwargs				,			**UpperCamelCase__				,			)

           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(UpperCamelCase__    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.sp_model.get_piece_size()







     def 						_a      (self    ):






           '''simple docstring'''



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







     def __getstate__(self    ):






           '''simple docstring'''



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







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

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

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







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.encode(UpperCamelCase__				,			out_type=UpperCamelCase__    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.piece_to_id(UpperCamelCase__    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.sp_model.IdToPiece(UpperCamelCase__    )
           return token







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           lowerCamelCase								=		""
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(UpperCamelCase__    ) + token
                       lowerCamelCase								=		[]
                 else:
                       current_sub_tokens.append(UpperCamelCase__    )
           out_string += self.sp_model.decode(UpperCamelCase__    )
           return out_string.strip()







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



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

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

           return (out_vocab_file,)




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch





@require_torch
@require_tf
class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		SMALL_MODEL_IDENTIFIER
           lowerCamelCase								=		"pt"
           lowerCamelCase								=		"tf"







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		AutoModel.from_pretrained(self.test_model    )
           model_pt.save_pretrained(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		TFAutoModel.from_pretrained(self.test_model				,			from_pt=__a    )
           model_tf.save_pretrained(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"mock_framework"

           # Framework provided - return whatever the user provides
           lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model				,			__a    )
           self.assertEqual(__a				,			__a    )

           # Local checkpoint and framework provided - return provided framework
           # PyTorch checkpoint
           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a				,			__a    )
                 self.assertEqual(__a				,			__a    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a				,			__a    )
                 self.assertEqual(__a				,			__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(__a    )
                 lowerCamelCase								=		FeaturesManager.determine_framework(__a    )
                 self.assertEqual(__a				,			self.framework_tf    )

           # Invalid local checkpoint
           with TemporaryDirectory() as local_invalid_ckpt:
                 with self.assertRaises(__a    ):
                       lowerCamelCase								=		FeaturesManager.determine_framework(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # PyTorch not in environment -> use TensorFlow
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_torch_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_tf    )

           # Both in environment -> use PyTorch
           lowerCamelCase								=		MagicMock(return_value=__a    )
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ), patch(
               "transformers.onnx.features.is_torch_available"				,			__a    ):
                 lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )
                 self.assertEqual(__a				,			self.framework_pt    )

           # Both not in environment -> raise error
           lowerCamelCase								=		MagicMock(return_value=__a    )
           lowerCamelCase								=		MagicMock(return_value=__a    )
           with patch("transformers.onnx.features.is_tf_available"				,			__a    ), patch(
               "transformers.onnx.features.is_torch_available"				,			__a    ):
                 with self.assertRaises(__a    ):
                       lowerCamelCase								=		FeaturesManager.determine_framework(self.test_model    )