


"""simple docstring"""





import unittest

import numpy as np

from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
     FlaxAlbertForMaskedLM,
     FlaxAlbertForMultipleChoice,
     FlaxAlbertForPreTraining,
     FlaxAlbertForQuestionAnswering,
     FlaxAlbertForSequenceClassification,
     FlaxAlbertForTokenClassification,
     FlaxAlbertModel,
 )



class 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    Tuple    , UpperCamelCase_     :    int    , UpperCamelCase_     :    List[Any]=1_3    , UpperCamelCase_     :    Tuple=7    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    List[Any]=True    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    List[str]=9_9    , UpperCamelCase_     :    Optional[int]=3_2    , UpperCamelCase_     :    str=5    , UpperCamelCase_     :    Optional[Any]=4    , UpperCamelCase_     :    List[str]=3_7    , UpperCamelCase_     :    List[Any]="gelu"    , UpperCamelCase_     :    Optional[int]=0.1    , UpperCamelCase_     :    Optional[int]=0.1    , UpperCamelCase_     :    Optional[Any]=5_1_2    , UpperCamelCase_     :    Optional[int]=1_6    , UpperCamelCase_     :    Dict=2    , UpperCamelCase_     :    str=0.02    , UpperCamelCase_     :    Any=4    , ):
           lowerCAmelCase    :		List[str]				=      parent
           lowerCAmelCase    :		List[Any]				=      batch_size
           lowerCAmelCase    :		Any				=      seq_length
           lowerCAmelCase    :		Dict				=      is_training
           lowerCAmelCase    :		Any				=      use_attention_mask
           lowerCAmelCase    :		Tuple				=      use_token_type_ids
           lowerCAmelCase    :		Any				=      use_labels
           lowerCAmelCase    :		Any				=      vocab_size
           lowerCAmelCase    :		Any				=      hidden_size
           lowerCAmelCase    :		Any				=      num_hidden_layers
           lowerCAmelCase    :		str				=      num_attention_heads
           lowerCAmelCase    :		Optional[int]				=      intermediate_size
           lowerCAmelCase    :		Tuple				=      hidden_act
           lowerCAmelCase    :		int				=      hidden_dropout_prob
           lowerCAmelCase    :		Optional[int]				=      attention_probs_dropout_prob
           lowerCAmelCase    :		Union[str, Any]				=      max_position_embeddings
           lowerCAmelCase    :		Any				=      type_vocab_size
           lowerCAmelCase    :		str				=      type_sequence_label_size
           lowerCAmelCase    :		List[str]				=      initializer_range
           lowerCAmelCase    :		Union[str, Any]				=      num_choices






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		List[Any]				=      ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

           lowerCAmelCase    :		int				=      None
           if self.use_attention_mask:
                 lowerCAmelCase    :		List[Any]				=      random_attention_mask([self.batch_size, self.seq_length] )

           lowerCAmelCase    :		Union[str, Any]				=      None
           if self.use_token_type_ids:
                 lowerCAmelCase    :		Dict				=      ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size )

           lowerCAmelCase    :		Optional[int]				=      AlbertConfig(
               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=UpperCamelCase_    , initializer_range=self.initializer_range    , )

           return config, input_ids, token_type_ids, attention_mask






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Any				=      self.prepare_config_and_inputs()
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		int				=      config_and_inputs
           lowerCAmelCase    :		Optional[Any]				=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict



@require_flax
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    (
         (
             FlaxAlbertModel,
             FlaxAlbertForPreTraining,
             FlaxAlbertForMaskedLM,
             FlaxAlbertForMultipleChoice,
             FlaxAlbertForQuestionAnswering,
             FlaxAlbertForSequenceClassification,
             FlaxAlbertForTokenClassification,
             FlaxAlbertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Optional[Any]				=      FlaxAlbertModelTester(self )






     @slow
     def    lowerCamelCase__   (		self     :    str ):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase    :		Union[str, Any]				=      model_class_name.from_pretrained('''albert-base-v2''' )
                 lowerCAmelCase    :		List[str]				=      model(np.ones((1, 1) ) )
                 self.assertIsNotNone(UpperCamelCase_ )



@require_flax
class 						snake_case_(				unittest.TestCase	):






     @slow
     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Tuple				=      FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
           lowerCAmelCase    :		List[str]				=      np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
           lowerCAmelCase    :		List[Any]				=      np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
           lowerCAmelCase    :		int				=      model(UpperCamelCase_    , attention_mask=UpperCamelCase_ )[0]
           lowerCAmelCase    :		Optional[Any]				=      (1, 1_1, 7_6_8)
           self.assertEqual(output.shape    , UpperCamelCase_ )
           lowerCAmelCase    :		Dict				=      np.array(
               [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )

           self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]    , UpperCamelCase_    , atol=1E-4 ) )






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


snake_case				:    Dict       				=     logging.get_logger(__name__)

snake_case				:    Tuple       				=     '''▁'''

snake_case				:    Any       				=     {'''vocab_file''': '''sentencepiece.bpe.model'''}

snake_case				:    Tuple       				=     {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

snake_case				:    int       				=     {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__      =       ['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
       a						:Optional[int] 							=       AddedToken(_lowerCamelCase					,  lstrip=_lowerCamelCase					,  rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase					,  _lowerCamelCase    ) else mask_token

       a						:int 							=       {} 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					,  )

       a						:Optional[int] 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.Load(str(_lowerCamelCase    )    )
       a						:str 							=       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'

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

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

       a						:Dict 							=       len(self.sp_model    ) + self.fairseq_offset
       a						:List[Any] 							=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




 def __getstate__(		self    ):
       a						:List[str] 							=       self.__dict__.copy()
       a						:Optional[int] 							=       None
       a						:int 							=       self.sp_model.serialized_model_proto()
       return state




 def __setstate__(		self					,  _lowerCamelCase    ):
       a						:Union[str, Any] 							=       d

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

       a						:Optional[int] 							=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )




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

       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       a						:List[Any] 							=       [self.cls_token_id]
       a						:Dict 							=       [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep




 def        SCREAMING_SNAKE_CASE__				(		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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       a						:int 							=       [self.sep_token_id]
       a						:int 							=       [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        SCREAMING_SNAKE_CASE__				(		self    ):
       return len(self.sp_model    ) + self.fairseq_offset + 1  # Add the <mask> token




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       {self.convert_ids_to_tokens(_lowerCamelCase    ): i for i in range(self.vocab_size    )}
       vocab.update(self.added_tokens_encoder    )
       return vocab




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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
       a						:Optional[Any] 							=       self.sp_model.PieceToId(_lowerCamelCase    )

       # 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					,  _lowerCamelCase    ):
       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					,  _lowerCamelCase    ):
       a						:Tuple 							=       ''''''.join(_lowerCamelCase    ).replace(_lowerCamelCase					,  ''' '''    ).strip()
       return out_string




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = None    ):
       if not os.path.isdir(_lowerCamelCase    ):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
             return
       a						:int 							=       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:
                   a						:List[Any] 							=       self.sp_model.serialized_model_proto()
                   fi.write(_lowerCamelCase    )

       return (out_vocab_file,)





