


'''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


__A	=logging.get_logger(__name__)

__A	={'vocab_file': 'spiece.model'}

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

__A	={'bert_for_seq_generation': 5_12}


class 		_snake_case					(     __SCREAMING_SNAKE_CASE	):
					lowerCAmelCase      :List[str]   =					VOCAB_FILES_NAMES
					lowerCAmelCase      :int   =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase      :List[str]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase      :List[Any]   =					[]
					lowerCAmelCase      :List[str]   =					['''input_ids''', '''attention_mask''']




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="<::::>"							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Optional[Any]        =     {} if sp_model_kwargs is None else sp_model_kwargs

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

								UpperCAmelCase__  :					List[str]        =     vocab_file

								UpperCAmelCase__  :					Dict        =     spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(_a)




					@property
					def 				snake_case__						(      self):
								return self.sp_model.get_piece_size()




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     {self.convert_ids_to_tokens(_a): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




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




					def __setstate__(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     d

								# for backward compatibility
								if not hasattr(self							,							"""sp_model_kwargs"""):
											UpperCAmelCase__  :					Optional[Any]        =     {}

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




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return self.sp_model.encode(_a							,							out_type=_a)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return self.sp_model.piece_to_id(_a)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     self.sp_model.IdToPiece(_a)
								return token




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Union[str, Any]        =     []
								UpperCAmelCase__  :					Union[str, Any]        =     """"""
								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(_a) + token
														UpperCAmelCase__  :					Dict        =     []
											else:
														current_sub_tokens.append(_a)
								out_string += self.sp_model.decode(_a)
								return out_string.strip()




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):




								if not os.path.isdir(_a):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__  :					Tuple        =     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:
														UpperCAmelCase__  :					Union[str, Any]        =     self.sp_model.serialized_model_proto()
														fi.write(_a)

								return (out_vocab_file,)



'''simple docstring'''



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     jnp.array(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))