





'''simple docstring'''

import os
import re
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


lowercase__						:			Tuple       =				logging.get_logger(__name__)

lowercase__						:			Tuple       =				{"""vocab_file""": """spiece.model"""}

lowercase__						:			int       =				{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

lowercase__						:			Tuple       =				{
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}
class 				__lowerCAmelCase     (    UpperCAmelCase__ ):






       """simple docstring"""
       _snake_case	:     Dict										=    VOCAB_FILES_NAMES
       _snake_case	:     Optional[Any]										=    PRETRAINED_VOCAB_FILES_MAP
       _snake_case	:     Optional[Any]										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _snake_case	:     Optional[Any]										=    ['input_ids', 'attention_mask']
       _snake_case	:     Dict										=    []





       def __init__(							self   :		int       , lowerCAmelCase__   :		Tuple       , lowerCAmelCase__   :		List[str]="<unk>"       , lowerCAmelCase__   :		List[str]="<s>"       , lowerCAmelCase__   :		Union[str, Any]="</s>"       , lowerCAmelCase__   :		int="<pad>"       , lowerCAmelCase__   :		Optional[Any]="[SEP]"       , lowerCAmelCase__   :		Optional[int]="[MASK]"       , lowerCAmelCase__   :		List[str]="[CLS]"       , lowerCAmelCase__   :		Optional[Any] = None       , **lowerCAmelCase__   :		Tuple       , )    ->     Tuple:

            '''simple docstring'''




            _UpperCamelCase				    =       AddedToken(_a       , lstrip=_a       , rstrip=_a     ) if isinstance(_a       , _a     ) else bos_token
            _UpperCamelCase				    =       AddedToken(_a       , lstrip=_a       , rstrip=_a     ) if isinstance(_a       , _a     ) else eos_token
            _UpperCamelCase				    =       AddedToken(_a       , lstrip=_a       , rstrip=_a     ) if isinstance(_a       , _a     ) else unk_token
            _UpperCamelCase				    =       AddedToken(_a       , lstrip=_a       , rstrip=_a     ) if isinstance(_a       , _a     ) else pad_token
            _UpperCamelCase				    =       AddedToken(_a       , lstrip=_a       , rstrip=_a     ) if isinstance(_a       , _a     ) else cls_token
            _UpperCamelCase				    =       AddedToken(_a       , lstrip=_a       , rstrip=_a     ) if isinstance(_a       , _a     ) else sep_token

            # Mask token behave like a normal word, i.e. include the space before it
            _UpperCamelCase				    =       AddedToken(_a       , lstrip=_a       , rstrip=_a     ) if isinstance(_a       , _a     ) else mask_token

            _UpperCamelCase				    =       {} if sp_model_kwargs is None else sp_model_kwargs

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

            _UpperCamelCase				    =       vocab_file

            _UpperCamelCase				    =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
            self.sp_model.Load(_a     )





       @property
       def snake_case__						(							self   :		List[Any]     )    ->     Optional[Any]:

            '''simple docstring'''




            return self.sp_model.get_piece_size()





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

            '''simple docstring'''




            _UpperCamelCase				    =       {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   :		List[Any]     )    ->     Union[str, Any]:

            '''simple docstring'''




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





       def __setstate__(							self   :		int       , lowerCAmelCase__   :		Tuple     )    ->     Optional[int]:

            '''simple docstring'''




            _UpperCamelCase				    =       d

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

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





       def snake_case__						(							self   :		Dict       , lowerCAmelCase__   :		Tuple     )    ->     Optional[Any]:

            '''simple docstring'''




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





       def snake_case__						(							self   :		Optional[Any]       , lowerCAmelCase__   :		Dict     )    ->     Tuple:

            '''simple docstring'''




            return self.sp_model.piece_to_id(_a     )





       def snake_case__						(							self   :		int       , lowerCAmelCase__   :		List[str]     )    ->     Tuple:

            '''simple docstring'''




            _UpperCamelCase				    =       self.sp_model.IdToPiece(_a     )
            return token





       def snake_case__						(							self   :		List[str]       , lowerCAmelCase__   :		Any     )    ->     Tuple:

            '''simple docstring'''




            _UpperCamelCase				    =       []
            _UpperCamelCase				    =       ''''''
            _UpperCamelCase				    =       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(_a     ) + token
                      _UpperCamelCase				    =       True
                      _UpperCamelCase				    =       []
                 else:
                      current_sub_tokens.append(_a     )
                      _UpperCamelCase				    =       False
            out_string += self.sp_model.decode(_a     )
            return out_string.strip()





       def snake_case__						(							self   :		Dict       , lowerCAmelCase__   :		List[Any]       , lowerCAmelCase__   :		List[str] = False       , lowerCAmelCase__   :		Any = None       , lowerCAmelCase__   :		Optional[Any] = True       , **lowerCAmelCase__   :		Tuple       , )    ->     Any:

            '''simple docstring'''




            _UpperCamelCase				    =       kwargs.pop('''use_source_tokenizer'''       , _a     )

            _UpperCamelCase				    =       self.convert_ids_to_tokens(_a       , skip_special_tokens=_a     )

            # To avoid mixing byte-level and unicode for byte-level BPT
            # we need to build string separately for added tokens and byte-level tokens
            # cf. https://github.com/huggingface/transformers/issues/1133
            _UpperCamelCase				    =       []
            _UpperCamelCase				    =       []
            for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                      continue
                 if token in self.added_tokens_encoder:
                      if current_sub_text:
                           sub_texts.append(self.convert_tokens_to_string(_a     )     )
                           _UpperCamelCase				    =       []
                      sub_texts.append(_a     )
                 else:
                      current_sub_text.append(_a     )
            if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(_a     )     )

            # Mimic the behavior of the Rust tokenizer:
            # No space before [MASK] and [SEP]
            if spaces_between_special_tokens:
                 _UpperCamelCase				    =       re.sub(r''' (\[(MASK|SEP)\])'''       , r'''\1'''       , ''' '''.join(_a     )     )
            else:
                 _UpperCamelCase				    =       ''''''.join(_a     )

            _UpperCamelCase				    =       (
                clean_up_tokenization_spaces
                if clean_up_tokenization_spaces is not None
                else self.clean_up_tokenization_spaces
            )
            if clean_up_tokenization_spaces:
                 _UpperCamelCase				    =       self.clean_up_tokenization(_a     )
                 return clean_text
            else:
                 return text





       def snake_case__						(							self   :		Union[str, Any]       , lowerCAmelCase__   :		Tuple       , lowerCAmelCase__   :		Tuple = None     )    ->     str:

            '''simple docstring'''




            if not os.path.isdir(_a     ):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
                 return
            _UpperCamelCase				    =       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				    =       self.sp_model.serialized_model_proto()
                      fi.write(_a     )

            return (out_vocab_file,)





       def snake_case__						(							self   :		Optional[int]       , lowerCAmelCase__   :		Optional[Any]       , lowerCAmelCase__   :		Tuple = None     )    ->     Dict:

            '''simple docstring'''




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





       def snake_case__						(							self   :		Any       , lowerCAmelCase__   :		int       , lowerCAmelCase__   :		str = None       , lowerCAmelCase__   :		Optional[Any] = False     )    ->     Any:

            '''simple docstring'''




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

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







       def snake_case__						(							self   :		List[str]       , lowerCAmelCase__   :		Optional[int]       , lowerCAmelCase__   :		List[str] = None     )    ->     Optional[int]:

            '''simple docstring'''




            _UpperCamelCase				    =       [self.sep_token_id]
            _UpperCamelCase				    =       [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     ) * [0] + len(token_ids_a + sep     ) * [1]









"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)






class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=7		, _a=True		, _a=True		, _a=True		, _a=True		, _a=99		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=512		, _a=16		, _a=2		, _a=0.02		, _a=4		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					seq_length
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_attention_mask
							lowerCamelCase			      =					use_token_type_ids
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					vocab_size
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					type_vocab_size
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					num_choices





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size )

							lowerCamelCase			      =					None
							if self.use_attention_mask:
										lowerCamelCase			      =					random_attention_mask([self.batch_size, self.seq_length] )

							lowerCamelCase			      =					None
							if self.use_token_type_ids:
										lowerCamelCase			      =					ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size )

							lowerCamelCase			      =					RoFormerConfig(
							    vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, is_decoder=_a		, initializer_range=self.initializer_range		, )

							return config, input_ids, token_type_ids, attention_mask





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict






@require_flax
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						True

				__UpperCamelCase			  =						(
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerModelTester(self )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_class_name in self.all_model_classes:
										lowerCamelCase			      =					model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""		, from_pt=_a )
										lowerCamelCase			      =					model(np.ones((1, 1) ) )
										self.assertIsNotNone(_a )






@require_flax
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
							lowerCamelCase			      =					jnp.array([[0, 1, 2, 3, 4, 5]] )
							lowerCamelCase			      =					model(_a )[0]

							lowerCamelCase			      =					50_000

							lowerCamelCase			      =					(1, 6, vocab_size)
							self.assertEqual(output.shape		, _a )

							lowerCamelCase			      =					jnp.array(
							    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

							self.assertTrue(jnp.allclose(output[:, :3, :3]		, _a		, atol=1e-4 ) )



